Table of Contents

TASKS

  1. predicting stage (SVM, Decision Trees, novel model)
  2. determining if there is a correlation with the disease and any other characstic (SVM, Decision Trees, novel model)
  3. determining whether or not there is any genetic ties (neural networks, novel model) (add this in)
  4. determine what type of disease is?
  5. Target the same dataset as the related works.

Walter

  • Neural Networks (parameter tuning + model + stats + feature reduction) EOW5
  • statistics/metrics (p-value, t-value, AUC) EOW7

Kris

  • Decision Trees (parameter tuning + model + stats + feature reduction) EOW5
  • discussion EOW7
  • conclusion EOW7
  • updates to PPT EOW8 (Jamie will help)

Thad

  • SVM (parameter tuning + model + stats + feature reduction) EOW5
  • rewrite of other papers EOW7
  • how this affects the community EOW7
  • tie back to other papers EOW7

Jamie

  • intro EOW4
  • explaining data points EOW4
  • data manipulation (all data related tasks) (reduction method) (reach if needed) EOW3
  • enhance pre-processing (feature correlations)
  • visualizations of ML Models (TBD if we have time) very novel EOW
  • application build EOW8
  • create new ML model EOW5
  • just see if we can find antoher dataset: found a separate one, too late for change
  • create a repo EOW3
  • one (minimum) paper for baseline (Walter/Thad will find 2-3 each) EOD Thursday
  • feature comparison for Logit & Chi2
  • change SMOTE (X_train) & Scaling (X_train & X_Test) after split

Primary Dataset

Cross, Simon S. "Dataset of Observed Features on Endoscopic Colorectal Biopsies from Normal Subjects and Patients with Chronic Inflammatory Bowel Disease (Crohn’s disease and Ulcerative Colitis)." Department of Pathology, University of Sheffield Medical School (1999): 1-15

Genetics Summary Statistics Dataset: https://www.ibdgenetics.org/downloads.html

East Asian DataSet: https://academic.oup.com/ecco-jcc/article/12/6/730/4951970#116848212

Libraries

In [1]:
# general libraries
import pandas as pd
import numpy as np
import itertools
import scipy.stats as stats
import random
import statistics
In [2]:
# data cleaning libraries
from collections import Counter
from imblearn.over_sampling import SMOTE
from sklearn.feature_selection import SelectKBest, mutual_info_classif, chi2
from sklearn.preprocessing import LabelEncoder, OrdinalEncoder, MinMaxScaler
In [3]:
# ML libraries
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, roc_auc_score
from sklearn.model_selection import KFold, train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
In [4]:
# Visualization libraries
from matplotlib import pyplot
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

Data

In [5]:
# toggle to hide code
from IPython.display import HTML

HTML('''<script>
code_show=true; 
function code_toggle() {
 if (code_show){
 $('div.input').hide();
 } else {
 $('div.input').show();
 }
 code_show = !code_show
} 
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')
Out[5]:
In [6]:
# center all images
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
HTML("""
<style>
.output_png {
    display: table-cell;
    text-align: center;
    vertical-align: middle;
}
</style>
""")
Out[6]:

Pull in Data

ENDSC Data

In [7]:
# all cases
all_cases = pd.read_excel("../Data/dataset/ENDOSC_1.xls", sheet_name="All cases")
# cleaned cases
cleaned_cases = pd.read_excel("../Data/dataset/ENDOSC_1_2_2.xls", sheet_name="All IBD&normal")
cleaned_cases_og = cleaned_cases
cleaned_cases.head()
Out[7]:
Year Lab No Age Sex Active inflammation? Mucosal surface Crypt architecture Crypt profiles Increased lamina propria cellularity? Mild & superficial increase in lamina propria cellularity? ... Mucin depletion Intraepithelial lymphocytes Subepithelial collagen Lamina propria granulomas Submucosal granulomas Basal histiocytic cells Confirmed diagnosis Method of confirmation Initial pathologists diagnosis Observing pathologists diagnosis
0 92 5213 20.536986 0 0 0 0 7 0 0 ... 0 0 0 0 0 0 UC Endoscopy Normal Normal
1 90 805 55.575342 1 1 1 3 4 1 0 ... 1 0 0 0 0 0 UC Endoscopy IBD ?UC Chronic idiopathic IBD - highly suggestive of ...
2 90 5957 58.698630 0 1 1 1 7 1 0 ... 0 0 0 0 0 0 UC Endoscopy IBD indeterminate, active Inflammation - unclassified
3 92 9207 65.934247 0 0 1 1 7 0 0 ... 0 0 0 0 0 0 UC Endoscopy Non-specific inflammation, chronic Chronic idiopathic IBD - indeterminate
4 95 14469 23.391781 0 0 0 0 7 0 0 ... 0 0 0 0 0 0 Crohns Endoscopy Non-specific inflammation, chronic Normal

5 rows × 29 columns

Data Definitions

In [8]:
IBD_def = pd.read_excel("../Data/Column_Definitions.xlsx", sheet_name="IBD")
In [9]:
IBD_def[["Data Column", "Definition", "In depth understanding"]]
Out[9]:
Data Column Definition In depth understanding
0 Year Year the biopsy was taken NaN
1 Lab No unique laboratory accession number for specifi... NaN
2 Age Age of patient NaN
3 Sex Sex of patient NaN
4 Active inflammation? Boolean NaN
5 Mucosal surface is there mucous on the stomach wall? flat, irregular, or villous projections\n\nThi...
6 Crypt architecture is low stomach present? is there prolific by crypt distortion ( branching, Basa...
7 Crypt profiles NaN no definition provided
8 Increased lamina propria cellularity? Its layers increased inflammation by inflammat... a thin layer of connective tissue that forms p...
9 Mild & superficial increase in lamina propria ... NaN Top layer only of mucus only?
10 Increased lymphoid aggregates in lamina propria? NaN A collection of B cells, T cells, and supporti...
11 Patchy lamina propria cellularity? Blocks Worked missingin some areas?
12 Marked & transmucosal increase in lamina propr... NaN Increase in mucus? - usually a sign of early UC
13 Cryptitis extent Cell inside it in the wall of the stomach inflammation of the intestinal crypts. The cry...
14 Cryptitis polymorphs The difference in shape changes in gland shapes
15 Crypt abscesses extent how severe is the ascess? Typically only present in chrons disease
16 Crypt abscesses polymorphs The form of an abscess what does the abscess look like?
17 Lamina propria polymorphs Outputs has mutation in the lamina propria
18 Epithelial changes The last layer is a layer in the wall of the s... changes in the first layer of the wlal?
19 Mucin depletion The layer of mucus secreted by the stomach was the mucin layer depleted?
20 Intraepithelial lymphocytes Lymphocytes occur with inflammation lymphocytes (white blood cells) found in the e...
21 Subepithelial collagen is needed if it increased, it is not normal below the outter layer of the outside wall of ...
22 Lamina propria granulomas Types of cells that are not present in Al-Tabi... small lumps of immune cells that form in your ...
23 Submucosal granulomas NaN "
24 Basal histiocytic cells NaN part of the immune system: vertebrate cell tha...
25 Confirmed diagnosis the confirmed and final diagnosis NaN
26 Method of confirmation how the diagnosis was come about NaN
27 Initial pathologists diagnosis intially, what the diagnosis was thought to be NaN
28 Observing pathologists diagnosis NaN NaN

Data Manipulation

IBD Stages: Since the stages of UC is determined by the severity of symptoms, the classes are manually added based on symptoms.

Use decision trees to determine

Perhaps find a doctor who can provide some expertise into the stages? - check if this is possible (we would need multiple people to have statistically significance)

Data Transformation Since the data is already dummy coded, the transformation of it will be required for understanding the outcome after modeling.

In [10]:
transform_dict = [{"data":["Mucin depletion", "Crypt architecture"], 
                   "definitions":[{
                       0: "Normal",
                       1: "Mild", 
                       2: "Moderate", 
                       3: "Severe"}]},
                  {"data": ["Cryptitis extent", "Crypt abscesses extent"],
                   "definitions": [{
                       0: "None",
                       1: "Little", 
                       2: "Moderate", 
                       3: "Marked"}]},
                  {"data": ["Lamina propria polymorphs"],
                  "definitions": [{
                      0: "Absent", 
                      1: "Focal", 
                      2: "Diffuse"}]},
                  {"data": ["Cryptitis polymorphs", "Crypt abscesses polymorphs"],
                   "definitions": [{
                       0: "None",
                       1: "Few", 
                       2: "Several", 
                       3: "Many"}]},
                  {"data": ["Epithelial changes"], 
                   "definitions": [{
                       0: "Normal",
                       1: "Flattening ",
                       2:  "Degeneration", 
                       3: "Erosion"}]},
                  {"data": ["Mucosal surface"], 
                   "definitions": [{
                       0: "Flat",
                       1: "Irregular", 
                       2: "Villous projections"}]}]

Set Seed for consistency

In [11]:
random.seed(123)

Crypt architecture measures the severity of the deformation of the colon, which will also signify at what severity stage the cases are at. This is the column that will be used for determining cases severities.

In [12]:
cleaned_cases['Crypt architecture'].unique()

crypt_dict = {0:"normal",
              1:"mild",
              2:"moderate",
              3:"severe"}

cleaned_cases['Severity of Crypt Arch'] = [crypt_dict[x] for x in cleaned_cases['Crypt architecture']]

'Severity of Crypt Arch' + 'diagnoses'
Out[12]:
'Severity of Crypt Archdiagnoses'

convert data to object rather than int since these are categorical data.

In [13]:
def change_to_object(df, data_col):
    df[data_col] = df[data_col].astype(object)

run = [change_to_object(cleaned_cases, c) for c in cleaned_cases.columns[3:]]
cleaned_cases['Crypt profiles'] = cleaned_cases['Crypt profiles'].astype('int')
cleaned_cases.dtypes
Out[13]:
Year                                                              int64
Lab No                                                            int64
Age                                                             float64
Sex                                                              object
Active inflammation?                                             object
Mucosal surface                                                  object
Crypt architecture                                               object
Crypt profiles                                                    int32
Increased lamina propria cellularity?                            object
Mild & superficial increase in lamina propria cellularity?       object
Increased lymphoid aggregates in lamina propria?                 object
Patchy lamina propria cellularity?                               object
Marked & transmucosal increase in lamina propria cellularity     object
Cryptitis extent                                                 object
Cryptitis polymorphs                                             object
Crypt abscesses extent                                           object
Crypt abscesses polymorphs                                       object
Lamina propria polymorphs                                        object
Epithelial changes                                               object
Mucin depletion                                                  object
Intraepithelial lymphocytes                                      object
Subepithelial collagen                                           object
Lamina propria granulomas                                        object
Submucosal granulomas                                            object
Basal histiocytic cells                                          object
Confirmed diagnosis                                              object
Method of confirmation                                           object
Initial pathologists diagnosis                                   object
Observing pathologists diagnosis                                 object
Severity of Crypt Arch                                           object
dtype: object

Data Cleaning

Clean Diagnosis: Strip data and Upper Case and ensure spelling of all are correct to prevent any separation of classes which are unnecessary.

In [14]:
print(cleaned_cases['Confirmed diagnosis'].unique())
cleaned_cases['Confirmed diagnosis'] = [c.strip().upper() for c in cleaned_cases['Confirmed diagnosis']]
print( cleaned_cases['Confirmed diagnosis'].unique())

['UC' 'Crohns' 'Normal' 'Uc' 'Normal ' 'Crohns ']
['UC' 'CROHNS' 'NORMAL']
In [15]:
cleaned_cases.columns
cleaned_cases["Method of confirmation"] = [x if x != "Endosocpy" else "Endoscopy" for x in cleaned_cases["Method of confirmation"]]
cleaned_cases["Method of confirmation"].unique()
Out[15]:
array(['Endoscopy', 'Resection', ' '], dtype=object)
In [16]:
cleaned_cases['Observing pathologists diagnosis'].unique()
Out[16]:
array(['Normal',
       'Chronic idiopathic IBD - highly suggestive of ulcerative colitis',
       'Inflammation - unclassified',
       'Chronic idiopathic IBD - indeterminate',
       "Chronic idiopathic IBD - highly suggestive of Crohn's disease",
       "Chronic idiopathic IBD - suggestive of Crohn's disease",
       'Chronic idiopathic IBD - suggestive of ulcerative colitis',
       'Infective type colitis', 'Other colitis Lymphocytic colitis',
       'Other colitis Melanosis coli'], dtype=object)
In [17]:
cleaned_cases['Initial pathologists diagnosis'].unique()
cleaned_cases['Initial pathologists diagnosis'] = [d if d != "IBD ?Crohn's" else "IBD ?Crohns" for d in cleaned_cases['Initial pathologists diagnosis']]
cleaned_cases['Initial pathologists diagnosis'] = [d if d not in ["Non-specific inflammation,chronic", "Non-specific inflammaton, chronic"] else "Non-specific inflammation, chronic" for d in cleaned_cases['Initial pathologists diagnosis']]
cleaned_cases['Initial pathologists diagnosis'].sort_values().unique()
Out[17]:
array(['?IBD ?Infective', 'Acute self-limiting colitis', 'Crohns',
       'Diversion colitis', 'IBD ?Crohns', 'IBD ?UC',
       'IBD indeterminate, active', 'IBD indeterminate, quiescent',
       'IBD indeterminate, quiscent', 'Inadequate', 'Melanosis coli',
       'Non-specific inflammation, acute',
       'Non-specific inflammation, chronic', 'Normal',
       'Normal - biopsy artefact', 'Normal - oedema',
       'Normal - spirochaetosis', 'Pouchitis', 'UC'], dtype=object)
In [18]:
cleaned_cases['Year'].sort_values().unique()
Out[18]:
array([87, 88, 89, 90, 91, 92, 93, 94, 95, 96], dtype=int64)

Missing/Duplicate Data Checks

There is no duplicates data

In [19]:
print(f'IBD duplicates: {cleaned_cases.duplicated().any()}')

IBD duplicates: False

There are no missing data values

In [20]:
print(f'IBD missing: {cleaned_cases.isnull().values.any()}')

IBD missing: False

Train Test Split

Cross and coworkers randomly shuffled the dataset and split the first 540 cases as the train set and the lasts 269 cases as the test set.

In [21]:
X = cleaned_cases.drop('Confirmed diagnosis',axis=1)
y = cleaned_cases['Confirmed diagnosis']
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=269, random_state=123)
print(f'Train set has {X_train.shape[0]} rows and test set has {X_test.shape[0]} rows')

Train set has 540 rows and test set has 269 rows

Class Imbalance

The minority class of heatlhy was oversampled so that there were equal diseased as unhealthy classes. This is also reflected in graphs below.

In [22]:
Counter(y_train)
Out[22]:
Counter({'UC': 305, 'CROHNS': 117, 'NORMAL': 118})
In [23]:
Counter(cleaned_cases['Initial pathologists diagnosis'])
Out[23]:
Counter({'Normal': 195,
         'IBD ?UC': 56,
         'IBD indeterminate, active': 122,
         'Non-specific inflammation, chronic': 39,
         'Crohns': 62,
         'UC': 219,
         'IBD indeterminate, quiescent': 67,
         'IBD ?Crohns': 19,
         'Normal - oedema': 6,
         'Non-specific inflammation, acute': 12,
         'IBD indeterminate, quiscent': 1,
         '?IBD ?Infective': 1,
         'Acute self-limiting colitis': 2,
         'Normal - spirochaetosis': 1,
         'Inadequate': 1,
         'Normal - biopsy artefact': 1,
         'Pouchitis': 1,
         'Melanosis coli': 3,
         'Diversion colitis': 1})

EDA

Jamie

The age is skewed towards the younger generations, and there are outliers of age under 15 and above 85. Since there is no proof that these age groups are errors opposed to only having a low count, they will be left in the data.

In [24]:
plt.figure(figsize=(10,10))
sns.distplot(cleaned_cases.Age.values, bins=50, kde=True)
plt.xlabel('Age', fontsize=12)
plt.show()

The data below shows that majority of the cases are from years 90-92 and 95-96. The other years have minimal contribution for years prior to year 90.

In [25]:
plt.figure(figsize=(10,10))
sns.distplot(cleaned_cases.Year.values, bins=50, kde=True)
plt.xlabel('Year', fontsize=12)
plt.show()

While the data is a mixture of both histology and endoscopy, but majority of the confirmation methods are endoscopy.

In [26]:
fig = px.histogram(cleaned_cases, x="Age", color='Method of confirmation')
fig.show()

Distribution of the dataset, where majority of the classes are UC and the remaining are split to normal and UC roughly evenly.

In [27]:
cd_gb = cleaned_cases.groupby("Confirmed diagnosis").count().reset_index()
fig = px.bar(cd_gb, x='Confirmed diagnosis', y='Year')
fig.show()
# cd_gb
In [28]:
fig = px.histogram(cleaned_cases, x="Age", color='Confirmed diagnosis')
fig.show()
plt.figure(figsize=(10,10))
Out[28]:
<Figure size 720x720 with 0 Axes>
<Figure size 720x720 with 0 Axes>

correlations

The correlation matrix is show below, which is no the same method which is used for continuous variable, but rather categorical variables.

In [29]:
corr_matrix = cleaned_cases.apply(lambda x : pd.factorize(x)[0]).corr(method='pearson', min_periods=1)
corr_matrix.head()
Out[29]:
Year Lab No Age Sex Active inflammation? Mucosal surface Crypt architecture Crypt profiles Increased lamina propria cellularity? Mild & superficial increase in lamina propria cellularity? ... Intraepithelial lymphocytes Subepithelial collagen Lamina propria granulomas Submucosal granulomas Basal histiocytic cells Confirmed diagnosis Method of confirmation Initial pathologists diagnosis Observing pathologists diagnosis Severity of Crypt Arch
Year 1.000000 -0.003860 -0.136752 0.013127 0.030828 0.006227 0.031643 -0.022099 0.042273 -0.041336 ... 0.028937 NaN 0.028572 -0.003668 0.042199 -0.009555 0.051771 -0.013499 0.082114 0.031643
Lab No -0.003860 1.000000 0.664152 -0.054323 0.022624 0.027877 -0.023491 0.030618 0.005793 -0.037130 ... -0.029240 NaN 0.002857 -0.019444 -0.069136 0.007435 0.054606 0.020831 -0.016912 -0.023491
Age -0.136752 0.664152 1.000000 -0.001607 -0.025780 -0.009704 -0.097190 -0.015083 -0.086089 -0.005274 ... -0.019805 NaN -0.036528 -0.045555 -0.063514 0.040530 0.070234 0.039240 -0.110549 -0.097190
Sex 0.013127 -0.054323 -0.001607 1.000000 -0.064066 -0.122315 -0.097119 -0.056975 -0.084777 -0.014896 ... 0.023621 NaN -0.005535 0.000962 -0.052319 0.142636 -0.032284 -0.071933 -0.057528 -0.097119
Active inflammation? 0.030828 0.022624 -0.025780 -0.064066 1.000000 0.369577 0.494637 0.210146 0.742688 0.085048 ... -0.013979 NaN 0.124816 0.035432 0.144034 -0.422155 0.155847 0.223316 0.434342 0.494637

5 rows × 30 columns

We see strong correlations between the symptoms. Specifically, there is a strong correlation between active inflammation and lamina propria polymorphs, which is investigated further below.

Many of the correlations are intuitively connected. For example, cryptis polymorphs and extent, since they are both related to the the fact of where there is inflammation in the linings of the stomach to the morphed cells of the glands.

One interesting obervation is the correlation of epithelial changes and the mucin depletions since the epithelial layer concerns the outter layer of the intestine and the mucin depletion primarily concerns with the inner side of the organ.

In [30]:
fig = px.imshow(corr_matrix)
fig.update_yaxes(visible=False, showticklabels=False)
fig.update_xaxes(visible=False, showticklabels=False)
fig.show()

Active inflammation and lamina propria polymorphs

Overall, the active inflamation makes sense considering if there is no inflammation, that there in turn would have no polymorphs. Since the inner linings are typically only shows to morph when there is inflammation, this is intuitive in the results.

In [31]:
sns.stripplot(x='Active inflammation?', y='Lamina propria polymorphs', data=cleaned_cases, jitter=True)
sns.despine()
In [32]:
%%time
sns.pairplot(cleaned_cases)

Wall time: 1min 7s
Out[32]:
<seaborn.axisgrid.PairGrid at 0x1f1e65dcfa0>

Odds Ratio

Odds ratio is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. source

There is no strong correlations between the two, that if a patient is of a specified year and age, there is a 1:1 ratio of the patient being diagnosed with UC of chrohns.

In [33]:
# odds ratio calc
uc_ch = cleaned_cases.loc[cleaned_cases["Confirmed diagnosis"].isin(['UC', 'CROHNS'])]
table_uc = uc_ch[["Confirmed diagnosis", "Year", "Age"]].groupby("Confirmed diagnosis").sum()#.values
print(table_uc)
oddsratio_uc, pvalue_uc = stats.fisher_exact(table_uc)
print("OddsR: ", round(oddsratio_uc,4), "p-Value:", pvalue_uc)

                      Year           Age
Confirmed diagnosis                     
CROHNS               15735   7288.413699
UC                   43525  22326.134247
OddsR:  1.1075 p-Value: 4.212365775994321e-10

Reducing categorical classes Since there isn't a high number of classes in each categorical columns, there is no need to reduce the number of classes in a categorical set.

Walter

Each column in the dataset is a symptom. SOme of the symptoms are rankings. When the column for Subepithelial collagen is 1 it means that the patient had that symptom and when it is 0 it means the patient did not have that symptom.

In [34]:
train = pd.concat([X_train,y_train],axis=1)

Supervised Learning

Get only the binary variables

In [35]:
binary_vars = X_train.columns[X_train.apply(lambda series: False if set(series)-{0,1} else True)]
binary_vars = list(set(binary_vars) - set(['Active inflammation?']))
# binary_vars

Calculate the relative risk ratio of having IBD if patient has or doesnt have Patchy lamina propria cellularity

In [36]:
patchyVsIbd   = train.groupby(['Patchy lamina propria cellularity?','Confirmed diagnosis']).size()
patchySummary = X_train.groupby('Patchy lamina propria cellularity?').size()
print(patchyVsIbd)
print(patchySummary)

Patchy lamina propria cellularity?  Confirmed diagnosis
0                                   CROHNS                  82
                                    NORMAL                 114
                                    UC                     257
1                                   CROHNS                  35
                                    NORMAL                   4
                                    UC                      48
dtype: int64
Patchy lamina propria cellularity?
0    453
1     87
dtype: int64

What proportion of those with patchy lamina propria had Crohn's Disease?

In [37]:
proportions = patchyVsIbd/patchySummary
proportions
Out[37]:
Patchy lamina propria cellularity?  Confirmed diagnosis
0                                   CROHNS                 0.181015
                                    NORMAL                 0.251656
                                    UC                     0.567329
1                                   CROHNS                 0.402299
                                    NORMAL                 0.045977
                                    UC                     0.551724
dtype: float64

How much more chance of getting Crohn's disease if you have patchy lamina propria cellularity VS if you dont have patch lamina prpria cellularity?

In [38]:
proportions.loc[1]/proportions.loc[0]
Out[38]:
Confirmed diagnosis
CROHNS    2.222456
NORMAL    0.182698
UC        0.972494
dtype: float64

Observe above that the probabilit of getting Crohn's is twice as much if you have patchy lamina propria cellularity VS if you dont have patchy lamina.

Determine relative risk of Crohn's or UC for all the symptoms
Calculation will require creating 3 tables:

  1. Symptom, Is Symptom Present, Confirmed Diagnosis, Count
  2. Symptom, Is Symptom Present, Count
  3. Symptom, Is Symptom Present, Confirmed Diagnosis, Proportion
  4. Symptom, Confirmed Diagnosis, Relative Risk (Final Table)
In [39]:
#1.Symptom, Is Symptom Present, Confirmed Diagnosis, Count
#Column, Value, Value for Diagnosis Column
binaryTrain = train[binary_vars+['Confirmed diagnosis']]
symptomDiagnosis = binaryTrain.reset_index().melt(id_vars=['index','Confirmed diagnosis'])
#Column, Value, Value for Diagnosis Column, Count
diseaseCountPerSymptom = symptomDiagnosis.groupby(['variable','value','Confirmed diagnosis']).size()
diseaseCountPerSymptom.head()
Out[39]:
variable                 value  Confirmed diagnosis
Basal histiocytic cells  0      CROHNS                 113
                                NORMAL                 117
                                UC                     291
                         1      CROHNS                   4
                                NORMAL                   1
dtype: int64
In [40]:
#2.Symptom, Is Symptom Present, Count
countPerSymptom = symptomDiagnosis.groupby(['variable','value']).size()
countPerSymptom.head()
Out[40]:
variable                                          value
Basal histiocytic cells                           0        521
                                                  1         19
Increased lamina propria cellularity?             0        216
                                                  1        324
Increased lymphoid aggregates in lamina propria?  0        453
dtype: int64
In [41]:
#3. Symptom, Is Symptom Present, Confirmed Diagnosis, Proportion
proportionIbdPerSymptom = diseaseCountPerSymptom/countPerSymptom
proportionIbdPerSymptom.head()
Out[41]:
variable                 value  Confirmed diagnosis
Basal histiocytic cells  0      CROHNS                 0.216891
                                NORMAL                 0.224568
                                UC                     0.558541
                         1      CROHNS                 0.210526
                                NORMAL                 0.052632
dtype: float64
In [42]:
#4.Symptom, Confirmed Diagnosis, Relative Risk (Final Table)
propDf = proportionIbdPerSymptom.reset_index()
noSymptom  = propDf.loc[propDf['value']==0].drop('value',axis=1).set_index(['variable','Confirmed diagnosis'])
yesSymptom = propDf.loc[propDf['value']==1].drop('value',axis=1).set_index(['variable','Confirmed diagnosis'])

'''
Some symptoms such as Submucosal granulomas are only present in Crohn's pateints. this means there is no 
patient who has both submucosal granuloma and UC.  So the risk of having UC given u have submcuoal granulomas
is 0.  But currently in the yesSymptom df, the row Submucosal granulom and UC does not even exist.  So if that row 
is missing just add a row with 0
'''  
noSymptom = noSymptom.reset_index()
varDxCombos= list(itertools.product(set(noSymptom['variable']),set(noSymptom['Confirmed diagnosis'])))
allCombos  = pd.DataFrame(index=pd.MultiIndex.from_tuples(varDxCombos)) 
allCombos.index.names = ['variable','Confirmed diagnosis']
noSymptom  = noSymptom.set_index(['variable','Confirmed diagnosis'])
yesSymptom = pd.merge(yesSymptom, allCombos, left_index=True, right_index=True, how='outer').fillna({0:0})
yesSymptom.head()
Out[42]:
0
variable Confirmed diagnosis
Basal histiocytic cells CROHNS 0.210526
NORMAL 0.052632
UC 0.736842
Increased lamina propria cellularity? CROHNS 0.225309
NORMAL 0.046296

Out of all the people that had Increased lamina propria cellularity, what percent of them had Crohn's disase?
In below table see that 22.5% of patients with Increased lamina propria cellularity had Crohn's disease.

Out of all the people that did NOT have Increase lamina propria cellularity, how many had Crohn's disease?

In [43]:
noSymptom = pd.merge(noSymptom, allCombos, left_index=True, right_index=True, how='outer').fillna({0:0})
noSymptom.head()
Out[43]:
0
variable Confirmed diagnosis
Basal histiocytic cells CROHNS 0.216891
NORMAL 0.224568
UC 0.558541
Increased lamina propria cellularity? CROHNS 0.203704
NORMAL 0.476852

You have two people, one with increased lamina propria cellularity and the other one without increased lamina propria cellularity. How much more likely is the first person to have Crohn's disease compared to the second?

You have two people, one with increased lamina propria cellularity and the other one without increased lamina propria cellularity. How much more likely is the first person to have Crohn's disease compared to the second?

In [44]:
relativeRiskIbd = (yesSymptom/noSymptom).reset_index()
relativeRiskIbd.head()
Out[44]:
variable Confirmed diagnosis 0
0 Basal histiocytic cells CROHNS 0.970657
1 Basal histiocytic cells NORMAL 0.234368
2 Basal histiocytic cells UC 1.319226
3 Increased lamina propria cellularity? CROHNS 1.106061
4 Increased lamina propria cellularity? NORMAL 0.097087
In [45]:
fig,ax = plt.subplots(nrows=1,ncols=3, sharey=True)
crohns = relativeRiskIbd[relativeRiskIbd['Confirmed diagnosis']=='CROHNS']
uc     = relativeRiskIbd[relativeRiskIbd['Confirmed diagnosis']=='UC']
normal = relativeRiskIbd[relativeRiskIbd['Confirmed diagnosis']=='NORMAL']
ax[0].barh(crohns['variable'],crohns[0])
ax[0].set(title='Crohns',
         ylabel='SYMPTOMS')
ax[1].barh(uc['variable'],uc[0])
ax[1].set(title='UC',xlabel='Relative Risk of Diagnosis Given That Patient has this Symptom',)
ax[2].barh(normal['variable'],normal[0])
ax[2].set(title='Normal')
Out[45]:
[Text(0.5, 1.0, 'Normal')]

For unsupervised EDA, The objective is to find multiple symptoms that are all 1 for the same patients and are all 0 for other patients.

  1. First, manually calculate the risk ratio between Symptom A and Symptom B
  2. Next, create a cross tab where the row is Symptom A, the column is Symptom B and the cell value is the risk ratio of Symptom B / Symptom A
  3. Finally, find the groups of symptoms that have highest risk ratios for one another. If 3 columns have high relative risk ratios, consider keeping only one of those columns and dropping the other 2

Risk Ratio

What is the risk of getting "Increased lamina propria cellularity" if you do have "Lamina propria granulomas" versus the risk of getting "Increased lamina propria cellularity" if you do not have "Lamina propria granulomas"?

If two symptoms are both positive in 1000 patients. And in another 1000 patients the two symptoms are negative. This would indicate correlation between those 2 symptoms.

In [46]:
exposure = 'Lamina propria granulomas'
disease  = 'Increased lamina propria cellularity?'
risks = X_train.groupby([exposure,disease]
               ).size()/X_train.groupby([exposure]).size()
risks = risks.reset_index()
riskGivenNoExposure = risks.loc[(risks[exposure] == 0)&
                              (risks[disease] == 1),0].values[0]
riskGivenExposure   = risks.loc[(risks[exposure] == 1)&
                              (risks[disease] == 1),0].values[0]
riskGivenExposure/riskGivenNoExposure
Out[46]:
1.5591715976331362

Get the cross tab of every symptom with every other symptom

In [47]:
def multicolumn_crosstab(df,cols):
    cols=sorted(cols)
    dummies = pd.get_dummies(df[cols])
    dfWithDummies = pd.concat([df,dummies],axis=1)
    dfWithDummies = dfWithDummies.reset_index()
    dfMelt = dfWithDummies.melt(id_vars=np.concatenate([np.array(['index']),dummies.columns.values]),
                      value_vars=cols)
    dfMelt = dfMelt.drop('index',axis=1)
    levelGroup = dfMelt.groupby(['variable','value'])
    crosstab = levelGroup.sum()
    countPerLevel = levelGroup.size()
    crossTabProp = crosstab.divide(countPerLevel,axis=0)
    return crossTabProp
In [48]:
ct = multicolumn_crosstab(X_train.astype(str),binary_vars)

In the below cross tab, the value in the second row, and in the fourth column (Incerased Lamina propria cellularity_1) is the number 0.894737. This means that 89% of the patients (in the train set) had both Basal histocytic cells and Increased lamina propria cellularity. Notice how this number 89% adds up tihe the 10.5263 % on the left of it. That 10% number is the proportion of patients that had basal histocytic cells but did NOT have icnreased lamina propria cellularity.

In [49]:
ct.head()
Out[49]:
Basal histiocytic cells_0 Basal histiocytic cells_1 Increased lamina propria cellularity?_0 Increased lamina propria cellularity?_1 Increased lymphoid aggregates in lamina propria?_0 Increased lymphoid aggregates in lamina propria?_1 Intraepithelial lymphocytes_0 Intraepithelial lymphocytes_1 Lamina propria granulomas_0 Lamina propria granulomas_1 ... Marked & transmucosal increase in lamina propria cellularity_1 Mild & superficial increase in lamina propria cellularity?_0 Mild & superficial increase in lamina propria cellularity?_1 Patchy lamina propria cellularity?_0 Patchy lamina propria cellularity?_1 Sex_0 Sex_1 Subepithelial collagen_0 Submucosal granulomas_0 Submucosal granulomas_1
variable value
Basal histiocytic cells 0 1.000000 0.000000 0.410749 0.589251 0.838772 0.161228 0.988484 0.011516 0.976967 0.023033 ... 0.272553 0.996161 0.003839 0.844530 0.155470 0.508637 0.491363 1.0 0.992322 0.007678
1 0.000000 1.000000 0.105263 0.894737 0.842105 0.157895 1.000000 0.000000 0.947368 0.052632 ... 0.421053 1.000000 0.000000 0.684211 0.315789 0.684211 0.315789 1.0 1.000000 0.000000
Increased lamina propria cellularity? 0 0.990741 0.009259 1.000000 0.000000 1.000000 0.000000 0.995370 0.004630 0.995370 0.004630 ... 0.000000 1.000000 0.000000 1.000000 0.000000 0.462963 0.537037 1.0 1.000000 0.000000
1 0.947531 0.052469 0.000000 1.000000 0.731481 0.268519 0.984568 0.015432 0.962963 0.037037 ... 0.462963 0.993827 0.006173 0.731481 0.268519 0.549383 0.450617 1.0 0.987654 0.012346
Increased lymphoid aggregates in lamina propria? 0 0.964680 0.035320 0.476821 0.523179 1.000000 0.000000 0.988962 0.011038 0.971302 0.028698 ... 0.331126 0.995585 0.004415 0.812362 0.187638 0.509934 0.490066 1.0 0.991170 0.008830

5 rows × 21 columns

Which feature is most correlated with the other features?

Observe that "Increased lamina propria cellularity" and "Active Inflammation" are the columns that is most correlated with the other symptoms.

noExposureDf: Get all the risks of getting Symptom B given that you dont have symptom A.
exposureDf: Get all the risks of getting Symptom B given that you do have symptom A.

In [50]:
crossTab     = ct.reset_index()
noExposureDf = crossTab.loc[crossTab['value']=='0']
exposureDf   = crossTab.loc[crossTab['value']=='1']

Divide all the risk-given-exposure/ risk-given-no-exposure to get the relative risk for every symptom pair

In [51]:
noExposureDf = noExposureDf.set_index('variable').drop('value',axis=1)
exposureDf   = exposureDf.set_index('variable').drop('value',axis=1)
relativeRisks= exposureDf/noExposureDf
relativeRisks.head()
Out[51]:
Basal histiocytic cells_0 Basal histiocytic cells_1 Increased lamina propria cellularity?_0 Increased lamina propria cellularity?_1 Increased lymphoid aggregates in lamina propria?_0 Increased lymphoid aggregates in lamina propria?_1 Intraepithelial lymphocytes_0 Intraepithelial lymphocytes_1 Lamina propria granulomas_0 Lamina propria granulomas_1 ... Marked & transmucosal increase in lamina propria cellularity_1 Mild & superficial increase in lamina propria cellularity?_0 Mild & superficial increase in lamina propria cellularity?_1 Patchy lamina propria cellularity?_0 Patchy lamina propria cellularity?_1 Sex_0 Sex_1 Subepithelial collagen_0 Submucosal granulomas_0 Submucosal granulomas_1
variable
Basal histiocytic cells 0.000000 inf 0.256272 1.518430 1.003974 0.979323 1.011650 0.000000 0.969703 2.285088 ... 1.544848 1.003854 0.0 0.810167 2.031189 1.345184 0.642681 1.0 1.007737 0.000000
Increased lamina propria cellularity? 0.956386 5.666667 0.000000 inf 0.731481 inf 0.989147 3.333333 0.967442 8.000000 ... inf 0.993827 inf 0.731481 inf 1.186667 0.839080 1.0 0.987654 inf
Increased lymphoid aggregates in lamina propria? 1.000868 0.976293 0.000000 1.911392 0.000000 inf 0.999538 1.041379 1.029545 0.000000 ... 0.000000 1.004435 0.0 1.202680 0.122515 1.059412 0.938180 1.0 1.008909 0.000000
Intraepithelial lymphocytes 1.036893 0.000000 0.413953 1.394984 0.993304 1.034884 0.000000 inf 0.852490 7.416667 ... 1.202703 1.003759 0.0 0.792873 2.094118 0.644928 1.379845 1.0 1.007547 0.000000
Lamina propria granulomas 0.955720 2.252137 0.188551 1.559172 1.197727 0.000000 0.931919 8.107692 0.000000 inf ... 0.000000 1.003810 0.0 0.089687 6.486154 1.356305 0.628503 1.0 0.770693 121.615385

5 rows × 21 columns

The relative risk from our risk matrix is the same as the one when we manually calculated it. 1.559171

In [52]:
relativeRisks.loc['Lamina propria granulomas','Increased lamina propria cellularity?_1' ]
Out[52]:
1.5591715976331362

Replace infinity values or abnormally high Relative risks with 0

In [53]:
relativeRisks = relativeRisks.applymap(lambda cell:0 if cell>20 else cell)

Observe in heatmap below that Submucosal granulomas are highly correlated with lamina propria granulomas

In [54]:
symptomPresent = [column for column in ct.columns if '1' in column]
sns.heatmap(relativeRisks[symptomPresent])
Out[54]:
<AxesSubplot:ylabel='variable'>

Out of the 453 patients that did not have patchy lamina propria cellularity none of those patients also had lamina propria granulomas.
However, out of the 87 patients that had patchy laminap propria cellularity, 4 of those patients also had lamina propria granulomas.
It looks like these 2 columns are correlated.

In [55]:
X_train.groupby(['Patchy lamina propria cellularity?','Submucosal granulomas']).size()
Out[55]:
Patchy lamina propria cellularity?  Submucosal granulomas
0                                   0                        453
1                                   0                         83
                                    1                          4
dtype: int64

Out of the 453 patients that did not have patchy lamina propria cellularity only 1 of those patients also had lamina propria granulomas.
However, out of the 87 patients that had patchy laminap propria cellularity, 12 of those patients also had lamina propria granulomas.
It looks like these 2 columns are correlated.

In [56]:
X_train.groupby(['Patchy lamina propria cellularity?','Lamina propria granulomas']).size()
Out[56]:
Patchy lamina propria cellularity?  Lamina propria granulomas
0                                   0                            452
                                    1                              1
1                                   0                             75
                                    1                             12
dtype: int64

You have two people, one with increased lamina propria cellularity and the other one without increased lamina propria cellularity. How much more likely is the first person to have Crohn's disease compared to the second?

In [57]:
train.groupby(['Submucosal granulomas','Confirmed diagnosis']).size()
Out[57]:
Submucosal granulomas  Confirmed diagnosis
0                      CROHNS                 113
                       NORMAL                 118
                       UC                     305
1                      CROHNS                   4
dtype: int64

Model Assumptions

The model assumptions for all models are not concerning to the data for visualization. The main requirement is that the data doesn't have a linear correlation between features and that the data is independent, assumed by the unique data points.

Due to the data primarily being categorical, the modifications/assumptions are difficult to decipher.

Data Prep for Modeling

Dummy coding the Data

There are two different set methods, dummy coding and ordinal. Before converting to dummy code, the data is first returned to it original form, then dummy coded to understand the effects of feature reduction and whether its required.

In [58]:
# convert it back to the original setup
df_reset = cleaned_cases.copy()
df_reset_od = cleaned_cases.copy()
df_reset.head()
Out[58]:
Year Lab No Age Sex Active inflammation? Mucosal surface Crypt architecture Crypt profiles Increased lamina propria cellularity? Mild & superficial increase in lamina propria cellularity? ... Intraepithelial lymphocytes Subepithelial collagen Lamina propria granulomas Submucosal granulomas Basal histiocytic cells Confirmed diagnosis Method of confirmation Initial pathologists diagnosis Observing pathologists diagnosis Severity of Crypt Arch
0 92 5213 20.536986 0 0 0 0 7 0 0 ... 0 0 0 0 0 UC Endoscopy Normal Normal normal
1 90 805 55.575342 1 1 1 3 4 1 0 ... 0 0 0 0 0 UC Endoscopy IBD ?UC Chronic idiopathic IBD - highly suggestive of ... severe
2 90 5957 58.698630 0 1 1 1 7 1 0 ... 0 0 0 0 0 UC Endoscopy IBD indeterminate, active Inflammation - unclassified mild
3 92 9207 65.934247 0 0 1 1 7 0 0 ... 0 0 0 0 0 UC Endoscopy Non-specific inflammation, chronic Chronic idiopathic IBD - indeterminate mild
4 95 14469 23.391781 0 0 0 0 7 0 0 ... 0 0 0 0 0 CROHNS Endoscopy Non-specific inflammation, chronic Normal normal

5 rows × 30 columns

In [59]:
transform_dict
Out[59]:
[{'data': ['Mucin depletion', 'Crypt architecture'],
  'definitions': [{0: 'Normal', 1: 'Mild', 2: 'Moderate', 3: 'Severe'}]},
 {'data': ['Cryptitis extent', 'Crypt abscesses extent'],
  'definitions': [{0: 'None', 1: 'Little', 2: 'Moderate', 3: 'Marked'}]},
 {'data': ['Lamina propria polymorphs'],
  'definitions': [{0: 'Absent', 1: 'Focal', 2: 'Diffuse'}]},
 {'data': ['Cryptitis polymorphs', 'Crypt abscesses polymorphs'],
  'definitions': [{0: 'None', 1: 'Few', 2: 'Several', 3: 'Many'}]},
 {'data': ['Epithelial changes'],
  'definitions': [{0: 'Normal',
    1: 'Flattening ',
    2: 'Degeneration',
    3: 'Erosion'}]},
 {'data': ['Mucosal surface'],
  'definitions': [{0: 'Flat', 1: 'Irregular', 2: 'Villous projections'}]}]
In [60]:
for val in transform_dict:
    print("============new dictionary===========")
    cols = val['data']
    print(val['definitions'])
    for col in cols:
        try:
            df_reset[col] = [val['definitions'][0][v] for v in df_reset[col]]
        except:
            pass

============new dictionary===========
[{0: 'Normal', 1: 'Mild', 2: 'Moderate', 3: 'Severe'}]
============new dictionary===========
[{0: 'None', 1: 'Little', 2: 'Moderate', 3: 'Marked'}]
============new dictionary===========
[{0: 'Absent', 1: 'Focal', 2: 'Diffuse'}]
============new dictionary===========
[{0: 'None', 1: 'Few', 2: 'Several', 3: 'Many'}]
============new dictionary===========
[{0: 'Normal', 1: 'Flattening ', 2: 'Degeneration', 3: 'Erosion'}]
============new dictionary===========
[{0: 'Flat', 1: 'Irregular', 2: 'Villous projections'}]
In [61]:
# review the data transformation
df_reset[["Cryptitis extent","Cryptitis polymorphs","Crypt abscesses extent","Crypt abscesses polymorphs","Lamina propria polymorphs","Epithelial changes","Mucin depletion"]]

# get dummies
dummied = pd.get_dummies(df_reset[["Cryptitis extent","Cryptitis polymorphs","Crypt abscesses extent","Crypt abscesses polymorphs","Lamina propria polymorphs","Epithelial changes","Mucin depletion","Method of confirmation","Initial pathologists diagnosis","Observing pathologists diagnosis", "Severity of Crypt Arch"]])
df_dummy = pd.merge(dummied, cleaned_cases.drop(["Cryptitis extent","Cryptitis polymorphs","Crypt abscesses extent","Crypt abscesses polymorphs","Lamina propria polymorphs","Epithelial changes","Mucin depletion","Method of confirmation","Initial pathologists diagnosis","Observing pathologists diagnosis", "Severity of Crypt Arch"], axis=1), how = "inner", left_index=True, right_index=True)

for column "Initial pathologists diagnosis_?IBD ?Infective", there is only one instance of this observation. Due to this we will drop the column as it will error during analysis.

In [62]:
df_dummy = df_dummy.drop(["Initial pathologists diagnosis_?IBD ?Infective","Initial pathologists diagnosis_Pouchitis", "Initial pathologists diagnosis_Diversion colitis", "Initial pathologists diagnosis_IBD indeterminate, quiscent"] ,axis=1)
In [63]:
df_type = pd.DataFrame(df_dummy.dtypes)

for x in df_type.loc[df_type[0] == "uint8"].reset_index()['index']:
    df_dummy[x] = df_dummy[x].astype('object')

df_dummy.dtypes
Out[63]:
Cryptitis extent_Little      object
Cryptitis extent_Marked      object
Cryptitis extent_Moderate    object
Cryptitis extent_None        object
Cryptitis polymorphs_Few     object
                              ...  
Subepithelial collagen       object
Lamina propria granulomas    object
Submucosal granulomas        object
Basal histiocytic cells      object
Confirmed diagnosis          object
Length: 78, dtype: object

Ordinal Data

Ordinal data is the method of which the data is already set up in. This allows the researchers to put the remaining data types into an ordinal set up for analysis.

In [64]:
ord_cols = ["Method of confirmation","Initial pathologists diagnosis","Observing pathologists diagnosis","Severity of Crypt Arch"]
In [65]:
for val in ord_cols:
    print(val)
    array_un = df_reset_od[val].unique().tolist()
    df_reset_od[val] = df_reset_od[val].apply(lambda x: array_un.index(x))

Method of confirmation
Initial pathologists diagnosis
Observing pathologists diagnosis
Severity of Crypt Arch
In [66]:
df_ordinal = df_reset_od
df_ordinal.head()
Out[66]:
Year Lab No Age Sex Active inflammation? Mucosal surface Crypt architecture Crypt profiles Increased lamina propria cellularity? Mild & superficial increase in lamina propria cellularity? ... Intraepithelial lymphocytes Subepithelial collagen Lamina propria granulomas Submucosal granulomas Basal histiocytic cells Confirmed diagnosis Method of confirmation Initial pathologists diagnosis Observing pathologists diagnosis Severity of Crypt Arch
0 92 5213 20.536986 0 0 0 0 7 0 0 ... 0 0 0 0 0 UC 0 0 0 0
1 90 805 55.575342 1 1 1 3 4 1 0 ... 0 0 0 0 0 UC 0 1 1 1
2 90 5957 58.698630 0 1 1 1 7 1 0 ... 0 0 0 0 0 UC 0 2 2 2
3 92 9207 65.934247 0 0 1 1 7 0 0 ... 0 0 0 0 0 UC 0 3 3 2
4 95 14469 23.391781 0 0 0 0 7 0 0 ... 0 0 0 0 0 CROHNS 0 3 0 0

5 rows × 30 columns

Of the two differing methods, one of the two will be selected for analysis.

Train/Test Split

In [67]:
X_ord = df_ordinal.drop("Confirmed diagnosis", axis=1)
y_ord = df_ordinal['Confirmed diagnosis']

X_train_ord , X_test_ord, y_train_ord, y_test_ord = train_test_split(X_ord, y_ord, test_size=0.25, random_state=42)
In [68]:
X_train_ord[X_train_ord.isna().any(axis=1)]
Out[68]:
Year Lab No Age Sex Active inflammation? Mucosal surface Crypt architecture Crypt profiles Increased lamina propria cellularity? Mild & superficial increase in lamina propria cellularity? ... Mucin depletion Intraepithelial lymphocytes Subepithelial collagen Lamina propria granulomas Submucosal granulomas Basal histiocytic cells Method of confirmation Initial pathologists diagnosis Observing pathologists diagnosis Severity of Crypt Arch

0 rows × 29 columns

Max-Min Transformation

In [69]:
df_ord_scale = df_reset_od.drop("Confirmed diagnosis", 1)
X_train_ord = X_train_ord.copy().reset_index().drop('index',axis=1)
X_test_ord = X_test_ord.copy().reset_index().drop('index',axis=1)
for val in X_train_ord.columns:
    X_train_ord[val] = X_train_ord[val].astype(int)
    X_test_ord[val] = X_test_ord[val].astype(int)
In [70]:
# Scale only columns that have values greater than 1
to_scale = [col for col in X_train_ord.columns if X_train_ord[col].max() > 1]
mms = MinMaxScaler()
scaled = mms.fit_transform(X_train_ord[to_scale])
scaled = pd.DataFrame(scaled, columns=to_scale)
scaled_test = mms.fit_transform(X_test_ord[to_scale])
scaled_test = pd.DataFrame(scaled_test, columns=to_scale)

# Replace original columns with scaled ones
for col in scaled:
    X_train_ord[col] = scaled[col]
    X_test_ord[col] = scaled_test[col]
    
# df_ord_scale = X_train_ord.merge(df_reset_od["Confirmed diagnosis"], how="inner", left_index=True, right_index=True)
In [71]:
# X_train_ord = X_train_ord.merge(df_reset_od["Confirmed diagnosis"], how="inner", left_index=True, right_index=True)
X_train_ord[X_train_ord.isna().any(axis=1)]
Out[71]:
Year Lab No Age Sex Active inflammation? Mucosal surface Crypt architecture Crypt profiles Increased lamina propria cellularity? Mild & superficial increase in lamina propria cellularity? ... Mucin depletion Intraepithelial lymphocytes Subepithelial collagen Lamina propria granulomas Submucosal granulomas Basal histiocytic cells Method of confirmation Initial pathologists diagnosis Observing pathologists diagnosis Severity of Crypt Arch

0 rows × 29 columns

In [72]:
X_test_ord[X_test_ord.isna().any(axis=1)]
Out[72]:
Year Lab No Age Sex Active inflammation? Mucosal surface Crypt architecture Crypt profiles Increased lamina propria cellularity? Mild & superficial increase in lamina propria cellularity? ... Mucin depletion Intraepithelial lymphocytes Subepithelial collagen Lamina propria granulomas Submucosal granulomas Basal histiocytic cells Method of confirmation Initial pathologists diagnosis Observing pathologists diagnosis Severity of Crypt Arch

0 rows × 29 columns

SMOTE

In [73]:
import smote_variants
In [139]:
'''
Create a msmote object that can upsample minority class.  Plug in our X train and y train as arrays.
Convert arrays back to pandas Series and dataframes.
PLayed around with proportion parameter until Normal matched UC,
had to do the MSMOTE twice one for each minority class (CROHNS and NORMAL)
'''

msm = smote_variants.MSMOTE(proportion=28, random_state = 123)
X_msm, y_msm = msm.sample(X_train_ord.values, y_train_ord.values)
X_msm = pd.DataFrame(columns = X_train_ord.columns, data=X_msm)
y_msm = pd.Series(data = y_msm)
y_msm.value_counts()

2021-05-29 09:36:39,306:INFO:MSMOTE: Running sampling via ('MSMOTE', "{'proportion': 28, 'n_neighbors': 5, 'n_jobs': 1, 'random_state': 123}")
Out[139]:
NORMAL    349
UC        348
CROHNS    133
dtype: int64
In [140]:
'''
Above notice how CROHNS is still 133 not equal to the other majority class of UC
SO we gotta do the MSMOTE again until CROHNS is equal to the other classes
'''
msm2 = smote_variants.MSMOTE(proportion=1, random_state = 123)
X_msm, y_msm = msm2.sample(X_msm.values, y_msm.values)
X_msm = pd.DataFrame(columns = X_train_ord.columns, data=X_msm)
y_msm = pd.Series(data = y_msm)
y_msm.value_counts()

2021-05-29 09:36:42,828:INFO:MSMOTE: Running sampling via ('MSMOTE', "{'proportion': 1, 'n_neighbors': 5, 'n_jobs': 1, 'random_state': 123}")
Out[140]:
CROHNS    349
NORMAL    349
UC        348
dtype: int64
In [118]:
print(f'''Shape of X before SMOTE: {X_ord.shape}
Shape of X after SMOTE: {X_msm.shape}''')

Shape of X before SMOTE: (809, 29)
Shape of X after SMOTE: (1046, 29)
In [119]:
sm = SMOTE(random_state=123)
In [120]:
X_sm, y_sm = sm.fit_resample(X_train_ord, y_train_ord)

print(f'''Shape of X before SMOTE: {X_ord.shape}
Shape of X after SMOTE: {X_sm.shape}''')

Shape of X before SMOTE: (809, 29)
Shape of X after SMOTE: (1044, 29)
In [138]:
print('\nBalance of positive and negative classes (%):')
y_sm.value_counts(normalize=True) * 100

Balance of positive and negative classes (%):
Out[138]:
UC        33.333333
CROHNS    33.333333
NORMAL    33.333333
Name: Confirmed diagnosis, dtype: float64
In [122]:
# Final Sets
data = [X_sm, X_test_ord, y_sm, y_test_ord]

Feature Importance

Chi-squared is used for determining feature importance. source

In [123]:
fs = SelectKBest(score_func=chi2, k='all')
fs.fit(X_sm.to_numpy() , y_sm.to_numpy() )
X_train_fs = fs.transform(X_sm.to_numpy() )
X_test_fs = fs.transform(X_test_ord.to_numpy())
In [124]:
df_features = pd.DataFrame()
df_features['Features'] = X_test_ord.columns
df_features['Scores'] = np.round(fs.scores_,2)
df_features = df_features.sort_values('Scores', ascending=False)
In [125]:
# plot the scores
ax = sns.barplot(x="Scores", y="Features", data=df_features)
In [126]:
# fit the model
# data = [X_sm, X_test_ord, y_sm, y_test_ord]
model = LogisticRegression(solver='lbfgs')
t = model.fit(X_sm, y_sm)
# evaluate the model
yhat = model.predict(X_test_ord)
# evaluate predictions
accuracy = accuracy_score(y_test_ord, yhat)
print('Accuracy: %.2f' % (accuracy*100))

Accuracy: 67.98

C:\Users\Walter\anaconda3\envs\ML7331\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
In [127]:
assigned = y_sm.unique()
assigned
Out[127]:
array(['UC', 'CROHNS', 'NORMAL'], dtype=object)
In [128]:
df_logit = pd.DataFrame()
df_logit['Attributes'] = X_sm.columns
for l in range(0, len(model.coef_)):
    df_logit['data_'+str(assigned[l])] = abs(model.coef_[l])
df_logit = df_logit.sort_values(by='data_UC', ascending = False)
# plot the scores
ax = sns.barplot(x="data_UC", y="Attributes", data=df_logit)
In [129]:
ax = sns.barplot(x="data_CROHNS", y="Attributes", data=df_logit)
In [130]:
ax = sns.barplot(x="data_NORMAL", y="Attributes", data=df_logit)

ETL PipeLine

confirmed to not be needed considering time constraint.

Machine Learning Models

Parameter Tuning

In [131]:
modelCompare = {'model': [], 'features': [], 'accuracy': [], 'f1': [], 
                'precision': [], 'recall': [], 'params': []}
In [132]:
def hyper_search(modelDictionary, modelParamDictionary, data, features):
    # define empty dictionaries to start
    modelAccuracy = 0
    bestModel = {}
    df_tmp = pd.DataFrame()
    modelCompare = pd.DataFrame()
    features1 = ', '.join(map(str, features))

    # iterate through the model dictionary to execute each model
    for key, value in modelDictionary.items():
        accuracyDics = {}
        finalResults = {}
        print(f'\r\nProcessing Model: {key}')

        # get the hyper parameter dictionary listings for the specific model
        paramDictionary = modelParamDictionary[key]

        # build out all permutations
        keys, values = zip(*paramDictionary.items())
        paramList = [dict(zip(keys, v)) for v in itertools.product(*values)]

        for dic in paramList:
            finalResults = main(value, data, dic)
            accuracyDics.update(groupClassifiers(finalResults))

        bestScore = 0
        avgAccuracy = 0
        plotScore = {}
        for k in accuracyDics:
            for a in accuracyDics[k][0]:
                k1 = {}
                k1 = k[:k.index('(')]
                avgAccuracy = statistics.mean(accuracyDics[k][0]['accuracy'])
                avgF1 = statistics.mean(accuracyDics[k][0]['f1'])
                avgPrecision = statistics.mean(accuracyDics[k][0]['precision'])
                avgRecall = statistics.mean(accuracyDics[k][0]['recall'])
                param = accuracyDics[k][0]['params']
                if avgAccuracy > bestScore:
                    bestScore = avgAccuracy
                    plotScore.clear()
                    plotScore = {'classifier': k1,
                                 'features': features1,
                                 'accuracy': accuracyDics[k][0]['accuracy'],
                                 'avgAccuracy': avgAccuracy,
                                 'f1': accuracyDics[k][0]['f1'],
                                 'avgF1': avgF1,
                                 'precision': accuracyDics[k][0]['precision'],
                                 'avgPrecision': avgPrecision,
                                 'recall': accuracyDics[k][0]['recall'],
                                 'avgRecall': avgRecall,
                                 'params': param}

        plot_models(plotScore)

        df_tmp = pd.DataFrame({'model': k1,
                       'features': features1,
                       'accuracy': avgAccuracy,
                       'f1': avgF1,
                       'precision': avgPrecision,
                       'recall': avgRecall,
                       'params': param})
        modelCompare = modelCompare.append(df_tmp, ignore_index=True)
        df_tmp = df_tmp[0:0]
        
        print(f'*****************************************************')
        print(f'* {key}')
        print(f'* Best Params Result: ')
        print(f'* {plotScore}')
        print(f'*****************************************************')
        if bestScore > modelAccuracy:
            modelAccuracy = avgAccuracy
            bestModel.clear()
            bestModel = plotScore
    print(f'*****************************************************')
    print(f'* Best Performing Model and Params is:')
    print(f'* {bestModel}')
    print(f'*****************************************************')

    print(f'\r\n{modelCompare}')
In [133]:
def main(clfr, data, clfrHyperParams={}):
    X_, y_, n_folds = data
    kf = KFold(n_splits=n_folds)
    ret = {}

    for id, (trainIndex, testIndex) in enumerate(kf.split(X_, y_)):
        clf = clfr(**clfrHyperParams)
        clf.fit(X_[trainIndex], y_[trainIndex])
        pred = clf.predict(X_[testIndex])
        ret[id] = {'classifier': clf,
                   'accuracy': accuracy_score(y_[testIndex], pred),
                   'f1': f1_score(y_[testIndex], pred, average='weighted'),
                   'precision': precision_score(y_[testIndex], pred, average='micro'),
                   'recall': recall_score(y_[testIndex], pred, average='micro'),
                   'params': clf.get_params(deep=True)}

    return ret
In [134]:
def groupClassifiers(resultsDict):
    accuraccyDict = {}

    for key in resultsDict:
        c = resultsDict[key]['classifier']
        a = resultsDict[key]['accuracy']
        f = resultsDict[key]['f1']
        p = resultsDict[key]['precision']
        r = resultsDict[key]['recall']
        params = resultsDict[key]['params']
        c_ = str(c).strip()

        # Then check if the string value 'c_' exists as a key in the dictionary
        if c_ in accuraccyDict:
            accuraccyDict[c_][0]['accuracy'].append(a)
            accuraccyDict[c_][0]['f1'].append(f)
            accuraccyDict[c_][0]['precision'].append(p)
            accuraccyDict[c_][0]['recall'].append(r)
        else:
            accuraccyDict[c_] = [{'accuracy': [a], 'f1': [f], 
                                  'precision': [p], 'recall': [r],
                                  'params': [params]}]

    return(accuraccyDict)
In [135]:
def plot_models(accuraccyDict):
    plt.rcParams.update ({'text.usetex': False,
                                 'font.family': 'stixgeneral',
                                 'mathtext.fontset': 'stix'})
    # create a new histogram with a given dictionary key's values
    fig = plt.figure(figsize=(8, 8))
    ax = fig.add_subplot(1, 1, 1)
    plt.hist(accuraccyDict['accuracy'], facecolor='green', alpha=0.75, bins=8)
    plt.text(.20, .5, 'Accuracy Score: ' + str(accuraccyDict['avgAccuracy']) + '\nF1 Score: ' + str(accuraccyDict['avgF1']))
    ax.set_title(accuraccyDict['classifier'], fontsize=15)
    ax.set_xlabel('Classifer Accuracy (By K-Fold)', fontsize=15)
    ax.set_ylabel('Frequency', fontsize=15)
    ax.xaxis.set_ticks(np.arange(0, 1.1, 0.1))
    ax.yaxis.set_ticks(np.arange(0, .5, 1))
    ax.xaxis.set_tick_params(labelsize=15)
    ax.yaxis.set_tick_params(labelsize=15)
    plt.subplots_adjust(left=0.125, right=0.9, bottom=0.1,
                        top=0.6, wspace=0.2, hspace=0.2)
    plt.show()
In [136]:
modelDictionary = {
    'RandomForestClassifier': RandomForestClassifier,
    'KNeighborsClassifier': KNeighborsClassifier,
    'LogisticRegression': LogisticRegression,
    'GaussianNB': GaussianNB,
    'AdaBoostClassifier': AdaBoostClassifier,
    'DecisionTreeClassifier': DecisionTreeClassifier,
    'SVC': SVC,
    'MLPClassifier': MLPClassifier
}
In [137]:
modelParamsDictionary = {
    'RandomForestClassifier': {   # https://sklearn.org/modules/generated/sklearn.ensemble.RandomForestClassifier.html
        'n_estimators': [200, 500, 700],
        'criterion': ['gini', 'entropy'],
        'max_features': ["auto", "sqrt", "log2"],
        'bootstrap': [True],
        'oob_score': [True, False],
        'n_jobs': [-1]
    },
    'KNeighborsClassifier': {  # https://sklearn.org/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
        'n_neighbors': np.arange(12, 18),
        'weights': ['uniform', 'distance'],
        'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],
        'n_jobs': [-1]
    },
    'LogisticRegression': {  # https://sklearn.org/modules/generated/sklearn.linear_model.LogisticRegression.html
        'C': [0.0001, 0.001, 1],
        'solver': ['newton-cg', 'lbfgs'],
        'multi_class': ['ovr', 'multinomial'],
        'max_iter': [100, 1000],
        'n_jobs': [-1]
    },
    'GaussianNB': {  # https://sklearn.org/modules/naive_bayes.html#gaussian-naive-bayes
        'var_smoothing': [1e-9]
    },
    'AdaBoostClassifier': {   # https://sklearn.org/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
        'n_estimators': [20, 50, 100, 300],
        'learning_rate': [1]
    },
    'DecisionTreeClassifier': {   # https://sklearn.org/modules/generated/sklearn.tree.DecisionTreeClassifier.html
        'criterion': ['gini', 'entropy'],
        'splitter': ['best', 'random'],
        'max_features': ["auto", "sqrt", "log2"]
    },
    'SVC': {    # https://sklearn.org/modules/generated/sklearn.svm.SVC.html
        'C': [0.0001, 0.001, 1.0],
        'kernel': ['linear'],
        'gamma': ['scale', 'auto'], 
        'cache_size': [4000]
    },
    'MLPClassifier': {    # https://sklearn.org/modules/generated/sklearn.neural_network.MLPClassifier.html
        'activation': ['identity', 'logistic'],
        'solver': ['adam'],
        'learning_rate': ['constant', 'invscaling', 'adaptive'],
        'max_iter': [5000, 7000, 9000]
    }
}
In [92]:
n_folds = 5
l = len(df_features['Features']) - 1
df = df_features['Features']

# SMOTE Dataset
X = pd.concat([X_sm, X_test_ord]) #.to_numpy()
y = pd.concat([y_sm, y_test_ord]).to_numpy()
#data = (X, y, n_folds)

print('********************************************')
print('Starting SMOTE data set....')
print('********************************************')

for i in range(l ,0, -1):
    col = []
    col = df[:i]
    nX = X.loc[:, col]
    nX = nX.to_numpy()
    data = (nX, y, n_folds)
    hyper_search(modelDictionary, modelParamsDictionary, data, col)

********************************************
Starting SMOTE data set....
********************************************

Processing Model: RandomForestClassifier
2021-05-28T17:26:56.586461 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.828, 0.86, 0.8634538152610441, 0.9678714859437751, 0.8232931726907631], 'avgAccuracy': 0.8685236947791165, 'f1': [0.8271091813919943, 0.8600174438108017, 0.8673740768060905, 0.977738120774916, 0.8197256613612385], 'avgF1': 0.8703928968290082, 'precision': [0.828, 0.86, 0.8634538152610441, 0.9678714859437751, 0.8232931726907631], 'avgPrecision': 0.8685236947791165, 'recall': [0.828, 0.86, 0.8634538152610441, 0.9678714859437751, 0.8232931726907631], 'avgRecall': 0.8685236947791165, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T17:27:05.496971 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.676, 0.708, 0.7429718875502008, 0.9357429718875502, 0.6586345381526104], 'avgAccuracy': 0.7442698795180723, 'f1': [0.6786593087166771, 0.718437668090492, 0.7535727847477373, 0.9550159287194385, 0.6719945557033038], 'avgF1': 0.7555360491955297, 'precision': [0.676, 0.708, 0.7429718875502008, 0.9357429718875502, 0.6586345381526104], 'avgPrecision': 0.7442698795180723, 'recall': [0.676, 0.708, 0.7429718875502008, 0.9357429718875502, 0.6586345381526104], 'avgRecall': 0.7442698795180723, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T17:27:25.146649 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.668, 0.724, 0.5863453815261044, 0.8795180722891566, 0.7309236947791165], 'avgAccuracy': 0.7177574297188755, 'f1': [0.6728897029260665, 0.7348372167764743, 0.5878572099425532, 0.903907983207618, 0.7348801455070901], 'avgF1': 0.7268744516719604, 'precision': [0.668, 0.724, 0.5863453815261044, 0.8795180722891566, 0.7309236947791165], 'avgPrecision': 0.7177574297188755, 'recall': [0.668, 0.724, 0.5863453815261044, 0.8795180722891566, 0.7309236947791165], 'avgRecall': 0.7177574297188755, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T17:27:25.693960 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgAccuracy': 0.6126457831325302, 'f1': [0.552681447124797, 0.6288689014773975, 0.2770385974126309, 0.7336636489018139, 0.657757820234779], 'avgF1': 0.5700020830302837, 'precision': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgPrecision': 0.6126457831325302, 'recall': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgRecall': 0.6126457831325302, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T17:27:34.386352 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.788, 0.764, 0.714859437751004, 0.8554216867469879, 0.5903614457831325], 'avgAccuracy': 0.7425285140562249, 'f1': [0.7850474403645135, 0.7776211704613379, 0.7220169663172444, 0.8938793296352566, 0.6072761154857532], 'avgF1': 0.7571682044528212, 'precision': [0.788, 0.764, 0.714859437751004, 0.8554216867469879, 0.5903614457831325], 'avgPrecision': 0.7425285140562249, 'recall': [0.788, 0.764, 0.714859437751004, 0.8554216867469879, 0.5903614457831325], 'avgRecall': 0.7425285140562249, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T17:27:35.414595 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.776, 0.784, 0.7590361445783133, 0.8835341365461847, 0.6666666666666666], 'avgAccuracy': 0.773847389558233, 'f1': [0.7778702548599419, 0.7844142402371753, 0.7668444549465655, 0.9172947063601807, 0.6868527407067109], 'avgF1': 0.7866552794221149, 'precision': [0.776, 0.784, 0.7590361445783133, 0.8835341365461847, 0.6666666666666666], 'avgPrecision': 0.773847389558233, 'recall': [0.776, 0.784, 0.7590361445783133, 0.8835341365461847, 0.6666666666666666], 'avgRecall': 0.773847389558233, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T17:27:38.199419 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.648, 0.728, 0.5823293172690763, 0.9036144578313253, 0.7108433734939759], 'avgAccuracy': 0.7145574297188755, 'f1': [0.6544530595187615, 0.7405095106913289, 0.5808435151715237, 0.9194715532200303, 0.7211868226421264], 'avgF1': 0.7232928922487541, 'precision': [0.648, 0.728, 0.5823293172690763, 0.9036144578313253, 0.7108433734939759], 'avgPrecision': 0.7145574297188755, 'recall': [0.648, 0.728, 0.5823293172690763, 0.9036144578313253, 0.7108433734939759], 'avgRecall': 0.7145574297188755, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T17:40:01.091615 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.768, 0.74, 0.6265060240963856, 0.9357429718875502, 0.7831325301204819], 'avgAccuracy': 0.7706763052208835, 'f1': [0.7713236031254607, 0.7521796426550246, 0.6304026173343259, 0.9512785775786806, 0.7821547244681772], 'avgF1': 0.7774678330323338, 'precision': [0.768, 0.74, 0.6265060240963856, 0.9357429718875502, 0.7831325301204819], 'avgPrecision': 0.7706763052208835, 'recall': [0.768, 0.74, 0.6265060240963856, 0.9357429718875502, 0.7831325301204819], 'avgRecall': 0.7706763052208835, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.828, 0.86, 0.8634538152610441, 0.9678714859437751, 0.8232931726907631], 'avgAccuracy': 0.8685236947791165, 'f1': [0.8271091813919943, 0.8600174438108017, 0.8673740768060905, 0.977738120774916, 0.8197256613612385], 'avgF1': 0.8703928968290082, 'precision': [0.828, 0.86, 0.8634538152610441, 0.9678714859437751, 0.8232931726907631], 'avgPrecision': 0.8685236947791165, 'recall': [0.828, 0.86, 0.8634538152610441, 0.9678714859437751, 0.8232931726907631], 'avgRecall': 0.8685236947791165, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.862908  0.865481   0.862908  0.862908   
1  0.735457  0.746901   0.735457  0.735457   
2  0.717757  0.726874   0.717757  0.717757   
3  0.612646  0.570002   0.612646  0.612646   
4  0.711290  0.729957   0.711290  0.711290   
5  0.752202  0.765450   0.752202  0.752202   
6  0.714557  0.723293   0.714557  0.714557   
7  0.770676  0.777468   0.770676  0.770676   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T17:47:09.267103 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.836, 0.852, 0.8755020080321285, 0.9678714859437751, 0.8072289156626506], 'avgAccuracy': 0.8677204819277109, 'f1': [0.836234625472066, 0.852, 0.8789221171959674, 0.977738120774916, 0.8062480817415697], 'avgF1': 0.8702285890369038, 'precision': [0.836, 0.852, 0.8755020080321285, 0.9678714859437751, 0.8072289156626506], 'avgPrecision': 0.8677204819277109, 'recall': [0.836, 0.852, 0.8755020080321285, 0.9678714859437751, 0.8072289156626506], 'avgRecall': 0.8677204819277109, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T17:47:16.746741 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.676, 0.708, 0.7429718875502008, 0.9357429718875502, 0.6586345381526104], 'avgAccuracy': 0.7442698795180723, 'f1': [0.6786593087166771, 0.718437668090492, 0.7535727847477373, 0.9550159287194385, 0.6719945557033038], 'avgF1': 0.7555360491955297, 'precision': [0.676, 0.708, 0.7429718875502008, 0.9357429718875502, 0.6586345381526104], 'avgPrecision': 0.7442698795180723, 'recall': [0.676, 0.708, 0.7429718875502008, 0.9357429718875502, 0.6586345381526104], 'avgRecall': 0.7442698795180723, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T17:47:31.113496 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.668, 0.724, 0.5863453815261044, 0.8795180722891566, 0.7309236947791165], 'avgAccuracy': 0.7177574297188755, 'f1': [0.6728897029260665, 0.7348372167764743, 0.5878572099425532, 0.903907983207618, 0.7348801455070901], 'avgF1': 0.7268744516719604, 'precision': [0.668, 0.724, 0.5863453815261044, 0.8795180722891566, 0.7309236947791165], 'avgPrecision': 0.7177574297188755, 'recall': [0.668, 0.724, 0.5863453815261044, 0.8795180722891566, 0.7309236947791165], 'avgRecall': 0.7177574297188755, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T17:47:31.491667 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgAccuracy': 0.6126457831325302, 'f1': [0.552681447124797, 0.6288689014773975, 0.2770385974126309, 0.7336636489018139, 0.657757820234779], 'avgF1': 0.5700020830302837, 'precision': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgPrecision': 0.6126457831325302, 'recall': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgRecall': 0.6126457831325302, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T17:47:39.018250 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.788, 0.764, 0.714859437751004, 0.8554216867469879, 0.5903614457831325], 'avgAccuracy': 0.7425285140562249, 'f1': [0.7850474403645135, 0.7776211704613379, 0.7220169663172444, 0.8938793296352566, 0.6072761154857532], 'avgF1': 0.7571682044528212, 'precision': [0.788, 0.764, 0.714859437751004, 0.8554216867469879, 0.5903614457831325], 'avgPrecision': 0.7425285140562249, 'recall': [0.788, 0.764, 0.714859437751004, 0.8554216867469879, 0.5903614457831325], 'avgRecall': 0.7425285140562249, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T17:47:39.852553 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.76, 0.78, 0.7831325301204819, 0.8795180722891566, 0.7228915662650602], 'avgAccuracy': 0.7851084337349398, 'f1': [0.7629434310995289, 0.7805793799665769, 0.7890713736307877, 0.9083998965622324, 0.7336151029463922], 'avgF1': 0.7949218368411036, 'precision': [0.76, 0.78, 0.7831325301204819, 0.8795180722891566, 0.7228915662650602], 'avgPrecision': 0.7851084337349398, 'recall': [0.76, 0.78, 0.7831325301204819, 0.8795180722891566, 0.7228915662650602], 'avgRecall': 0.7851084337349398, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T17:47:42.136415 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.648, 0.728, 0.5823293172690763, 0.9036144578313253, 0.7108433734939759], 'avgAccuracy': 0.7145574297188755, 'f1': [0.6544530595187615, 0.7405095106913289, 0.5808435151715237, 0.9194715532200303, 0.7211868226421264], 'avgF1': 0.7232928922487541, 'precision': [0.648, 0.728, 0.5823293172690763, 0.9036144578313253, 0.7108433734939759], 'avgPrecision': 0.7145574297188755, 'recall': [0.648, 0.728, 0.5823293172690763, 0.9036144578313253, 0.7108433734939759], 'avgRecall': 0.7145574297188755, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T17:59:35.926953 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.764, 0.724, 0.6224899598393574, 0.9397590361445783, 0.7710843373493976], 'avgAccuracy': 0.7642666666666666, 'f1': [0.7676557730961064, 0.736180582846288, 0.6251461742638138, 0.957102917494551, 0.771675097086404], 'avgF1': 0.7715521089574326, 'precision': [0.764, 0.724, 0.6224899598393574, 0.9397590361445783, 0.7710843373493976], 'avgPrecision': 0.7642666666666666, 'recall': [0.764, 0.724, 0.6224899598393574, 0.9397590361445783, 0.7710843373493976], 'avgRecall': 0.7642666666666666, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.836, 0.852, 0.8755020080321285, 0.9678714859437751, 0.8072289156626506], 'avgAccuracy': 0.8677204819277109, 'f1': [0.836234625472066, 0.852, 0.8789221171959674, 0.977738120774916, 0.8062480817415697], 'avgF1': 0.8702285890369038, 'precision': [0.836, 0.852, 0.8755020080321285, 0.9678714859437751, 0.8072289156626506], 'avgPrecision': 0.8677204819277109, 'recall': [0.836, 0.852, 0.8755020080321285, 0.9678714859437751, 0.8072289156626506], 'avgRecall': 0.8677204819277109, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.863708  0.866081   0.863708  0.863708   
1  0.735457  0.746901   0.735457  0.735457   
2  0.717757  0.726874   0.717757  0.717757   
3  0.612646  0.570002   0.612646  0.612646   
4  0.711290  0.729957   0.711290  0.711290   
5  0.737780  0.755380   0.737780  0.737780   
6  0.714557  0.723293   0.714557  0.714557   
7  0.752209  0.758658   0.752209  0.752209   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T18:06:48.743134 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.828, 0.864, 0.8594377510040161, 0.9437751004016064, 0.7951807228915663], 'avgAccuracy': 0.8580787148594378, 'f1': [0.8285527797833936, 0.8655037593984962, 0.8637273320226587, 0.9612567250285755, 0.7979282888502476], 'avgF1': 0.8633937770166743, 'precision': [0.828, 0.864, 0.8594377510040161, 0.9437751004016064, 0.7951807228915663], 'avgPrecision': 0.8580787148594378, 'recall': [0.828, 0.864, 0.8594377510040161, 0.9437751004016064, 0.7951807228915663], 'avgRecall': 0.8580787148594378, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T18:06:56.214821 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.676, 0.736, 0.7228915662650602, 0.9317269076305221, 0.6746987951807228], 'avgAccuracy': 0.748263453815261, 'f1': [0.6815873833279692, 0.7458660493006971, 0.7325713108845638, 0.9469670815364419, 0.6915493530831287], 'avgF1': 0.7597082356265602, 'precision': [0.676, 0.736, 0.7228915662650602, 0.9317269076305221, 0.6746987951807228], 'avgPrecision': 0.748263453815261, 'recall': [0.676, 0.736, 0.7228915662650602, 0.9317269076305221, 0.6746987951807228], 'avgRecall': 0.748263453815261, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
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Processing Model: LogisticRegression
2021-05-28T18:07:10.506441 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.66, 0.712, 0.570281124497992, 0.8955823293172691, 0.7309236947791165], 'avgAccuracy': 0.7137574297188755, 'f1': [0.665707475358269, 0.72396834625323, 0.5700808975480696, 0.9184093070611958, 0.7336770034782398], 'avgF1': 0.7223686059398009, 'precision': [0.66, 0.712, 0.570281124497992, 0.8955823293172691, 0.7309236947791165], 'avgPrecision': 0.7137574297188755, 'recall': [0.66, 0.712, 0.570281124497992, 0.8955823293172691, 0.7309236947791165], 'avgRecall': 0.7137574297188755, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T18:07:10.916821 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgAccuracy': 0.6126457831325302, 'f1': [0.552681447124797, 0.6288689014773975, 0.2770385974126309, 0.7336636489018139, 0.657757820234779], 'avgF1': 0.5700020830302837, 'precision': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgPrecision': 0.6126457831325302, 'recall': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgRecall': 0.6126457831325302, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T18:07:17.700846 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.708, 0.76, 0.714859437751004, 0.8594377510040161, 0.6666666666666666], 'avgAccuracy': 0.7417927710843374, 'f1': [0.7160322689186333, 0.768625538020086, 0.7194030676010408, 0.9056605430468228, 0.6830181038363478], 'avgF1': 0.7585479042845861, 'precision': [0.708, 0.76, 0.714859437751004, 0.8594377510040161, 0.6666666666666666], 'avgPrecision': 0.7417927710843374, 'recall': [0.708, 0.76, 0.714859437751004, 0.8594377510040161, 0.6666666666666666], 'avgRecall': 0.7417927710843374, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-28T18:07:18.520232 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.76, 0.748, 0.7630522088353414, 0.8835341365461847, 0.7228915662650602], 'avgAccuracy': 0.7754955823293173, 'f1': [0.7624401600437734, 0.7529442970822281, 0.7676948106967192, 0.9138461870967235, 0.7301485697572015], 'avgF1': 0.7854148049353291, 'precision': [0.76, 0.748, 0.7630522088353414, 0.8835341365461847, 0.7228915662650602], 'avgPrecision': 0.7754955823293173, 'recall': [0.76, 0.748, 0.7630522088353414, 0.8835341365461847, 0.7228915662650602], 'avgRecall': 0.7754955823293173, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-28T18:07:20.887143 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.64, 0.736, 0.5863453815261044, 0.8995983935742972, 0.714859437751004], 'avgAccuracy': 0.7153606425702811, 'f1': [0.6458142818832656, 0.7486072888025881, 0.5858400307064529, 0.9168629230017248, 0.7237293142787878], 'avgF1': 0.7241707677345638, 'precision': [0.64, 0.736, 0.5863453815261044, 0.8995983935742972, 0.714859437751004], 'avgPrecision': 0.7153606425702811, 'recall': [0.64, 0.736, 0.5863453815261044, 0.8995983935742972, 0.714859437751004], 'avgRecall': 0.7153606425702811, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T18:19:06.092171 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.764, 0.728, 0.6144578313253012, 0.8955823293172691, 0.7550200803212851], 'avgAccuracy': 0.7514120481927711, 'f1': [0.7680762888085839, 0.7403451757127061, 0.6137999433975126, 0.9216021677928304, 0.7588970522692465], 'avgF1': 0.7605441255961759, 'precision': [0.764, 0.728, 0.6144578313253012, 0.8955823293172691, 0.7550200803212851], 'avgPrecision': 0.7514120481927711, 'recall': [0.764, 0.728, 0.6144578313253012, 0.8955823293172691, 0.7550200803212851], 'avgRecall': 0.7514120481927711, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.828, 0.864, 0.8594377510040161, 0.9437751004016064, 0.7951807228915663], 'avgAccuracy': 0.8580787148594378, 'f1': [0.8285527797833936, 0.8655037593984962, 0.8637273320226587, 0.9612567250285755, 0.7979282888502476], 'avgF1': 0.8633937770166743, 'precision': [0.828, 0.864, 0.8594377510040161, 0.9437751004016064, 0.7951807228915663], 'avgPrecision': 0.8580787148594378, 'recall': [0.828, 0.864, 0.8594377510040161, 0.9437751004016064, 0.7951807228915663], 'avgRecall': 0.8580787148594378, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.854895  0.859369   0.854895  0.854895   
1  0.744263  0.755206   0.744263  0.744263   
2  0.713757  0.722369   0.713757  0.713757   
3  0.612646  0.570002   0.612646  0.612646   
4  0.708129  0.726576   0.708129  0.708129   
5  0.720138  0.736696   0.720138  0.720138   
6  0.715361  0.724171   0.715361  0.715361   
7  0.729006  0.737930   0.729006  0.729006   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T18:28:27.072165 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.812, 0.836, 0.8674698795180723, 0.8875502008032129, 0.7429718875502008], 'avgAccuracy': 0.8291983935742973, 'f1': [0.813200906871294, 0.8360664842854237, 0.8718665613811093, 0.9155551595926769, 0.7559460144319022], 'avgF1': 0.8385270253124812, 'precision': [0.812, 0.836, 0.8674698795180723, 0.8875502008032129, 0.7429718875502008], 'avgPrecision': 0.8291983935742973, 'recall': [0.812, 0.836, 0.8674698795180723, 0.8875502008032129, 0.7429718875502008], 'avgRecall': 0.8291983935742973, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T18:28:38.890529 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.68, 0.736, 0.7429718875502008, 0.9076305220883534, 0.6947791164658634], 'avgAccuracy': 0.7522763052208835, 'f1': [0.6877526715625929, 0.7480337081311398, 0.7522077879922301, 0.9228590663230958, 0.7094242322327077], 'avgF1': 0.7640554932483532, 'precision': [0.68, 0.736, 0.7429718875502008, 0.9076305220883534, 0.6947791164658634], 'avgPrecision': 0.7522763052208835, 'recall': [0.68, 0.736, 0.7429718875502008, 0.9076305220883534, 0.6947791164658634], 'avgRecall': 0.7522763052208835, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T18:29:01.690892 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.644, 0.712, 0.5542168674698795, 0.8955823293172691, 0.7068273092369478], 'avgAccuracy': 0.7025253012048193, 'f1': [0.6518589696018269, 0.7225705574912893, 0.5513998094764601, 0.9184093070611958, 0.7125847337938117], 'avgF1': 0.7113646754849168, 'precision': [0.644, 0.712, 0.5542168674698795, 0.8955823293172691, 0.7068273092369478], 'avgPrecision': 0.7025253012048193, 'recall': [0.644, 0.712, 0.5542168674698795, 0.8955823293172691, 0.7068273092369478], 'avgRecall': 0.7025253012048193, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T18:29:02.570184 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgAccuracy': 0.6126457831325302, 'f1': [0.552681447124797, 0.6288689014773975, 0.2770385974126309, 0.7336636489018139, 0.657757820234779], 'avgF1': 0.5700020830302837, 'precision': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgPrecision': 0.6126457831325302, 'recall': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgRecall': 0.6126457831325302, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T18:29:15.507558 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.776, 0.76, 0.7349397590361446, 0.8835341365461847, 0.6224899598393574], 'avgAccuracy': 0.7553927710843373, 'f1': [0.7744280701754386, 0.7697275576036867, 0.7414945152311886, 0.9214400791014892, 0.6375871493358014], 'avgF1': 0.7689354742895209, 'precision': [0.776, 0.76, 0.7349397590361446, 0.8835341365461847, 0.6224899598393574], 'avgPrecision': 0.7553927710843373, 'recall': [0.776, 0.76, 0.7349397590361446, 0.8835341365461847, 0.6224899598393574], 'avgRecall': 0.7553927710843373, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 100, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T18:29:16.992090 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.732, 0.776, 0.8393574297188755, 0.8594377510040161, 0.6626506024096386], 'avgAccuracy': 0.773889156626506, 'f1': [0.7372749777064384, 0.7826571018651364, 0.8440899595092938, 0.8951397345521789, 0.6860305896898781], 'avgF1': 0.789038472664585, 'precision': [0.732, 0.776, 0.8393574297188755, 0.8594377510040161, 0.6626506024096386], 'avgPrecision': 0.773889156626506, 'recall': [0.732, 0.776, 0.8393574297188755, 0.8594377510040161, 0.6626506024096386], 'avgRecall': 0.773889156626506, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T18:29:19.817111 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.644, 0.732, 0.5983935742971888, 0.8995983935742972, 0.7108433734939759], 'avgAccuracy': 0.7169670682730923, 'f1': [0.6498693882465545, 0.7450254584392987, 0.597733873410639, 0.9168629230017248, 0.7201352074772971], 'avgF1': 0.7259253701151028, 'precision': [0.644, 0.732, 0.5983935742971888, 0.8995983935742972, 0.7108433734939759], 'avgPrecision': 0.7169670682730923, 'recall': [0.644, 0.732, 0.5983935742971888, 0.8995983935742972, 0.7108433734939759], 'avgRecall': 0.7169670682730923, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T18:41:31.293042 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.748, 0.728, 0.6104417670682731, 0.927710843373494, 0.7710843373493976], 'avgAccuracy': 0.7570473895582329, 'f1': [0.7516048030524071, 0.7388493580885099, 0.6093513707811818, 0.9413247968953751, 0.7726428768326858], 'avgF1': 0.762754641130032, 'precision': [0.748, 0.728, 0.6104417670682731, 0.927710843373494, 0.7710843373493976], 'avgPrecision': 0.7570473895582329, 'recall': [0.748, 0.728, 0.6104417670682731, 0.927710843373494, 0.7710843373493976], 'avgRecall': 0.7570473895582329, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.812, 0.836, 0.8674698795180723, 0.8875502008032129, 0.7429718875502008], 'avgAccuracy': 0.8291983935742973, 'f1': [0.813200906871294, 0.8360664842854237, 0.8718665613811093, 0.9155551595926769, 0.7559460144319022], 'avgF1': 0.8385270253124812, 'precision': [0.812, 0.836, 0.8674698795180723, 0.8875502008032129, 0.7429718875502008], 'avgPrecision': 0.8291983935742973, 'recall': [0.812, 0.836, 0.8674698795180723, 0.8875502008032129, 0.7429718875502008], 'avgRecall': 0.8291983935742973, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.820392  0.829719   0.820392  0.820392   
1  0.744270  0.755875   0.744270  0.744270   
2  0.702525  0.711365   0.702525  0.702525   
3  0.612646  0.570002   0.612646  0.612646   
4  0.726496  0.737882   0.726496  0.726496   
5  0.765115  0.777071   0.765115  0.765115   
6  0.716967  0.725925   0.716967  0.716967   
7  0.744993  0.752546   0.744993  0.744993   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T18:48:57.842007 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.8, 0.844, 0.8554216867469879, 0.8875502008032129, 0.751004016064257], 'avgAccuracy': 0.8275951807228916, 'f1': [0.8012799525504152, 0.8450348370675508, 0.860061364742629, 0.9138940034074152, 0.7625731465438117], 'avgF1': 0.8365686608623644, 'precision': [0.8, 0.844, 0.8554216867469879, 0.8875502008032129, 0.751004016064257], 'avgPrecision': 0.8275951807228916, 'recall': [0.8, 0.844, 0.8554216867469879, 0.8875502008032129, 0.751004016064257], 'avgRecall': 0.8275951807228916, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T18:49:05.581181 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.684, 0.744, 0.7349397590361446, 0.9116465863453815, 0.7028112449799196], 'avgAccuracy': 0.7554795180722892, 'f1': [0.692163158596772, 0.7556137767783171, 0.7442694804690071, 0.9268751305801239, 0.718436433921286], 'avgF1': 0.7674715960691012, 'precision': [0.684, 0.744, 0.7349397590361446, 0.9116465863453815, 0.7028112449799196], 'avgPrecision': 0.7554795180722892, 'recall': [0.684, 0.744, 0.7349397590361446, 0.9116465863453815, 0.7028112449799196], 'avgRecall': 0.7554795180722892, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T18:49:19.527188 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.64, 0.716, 0.5542168674698795, 0.8955823293172691, 0.7068273092369478], 'avgAccuracy': 0.7025253012048193, 'f1': [0.6475787243787932, 0.7260595546558705, 0.5513998094764601, 0.9184093070611958, 0.7125847337938117], 'avgF1': 0.7112064258732262, 'precision': [0.64, 0.716, 0.5542168674698795, 0.8955823293172691, 0.7068273092369478], 'avgPrecision': 0.7025253012048193, 'recall': [0.64, 0.716, 0.5542168674698795, 0.8955823293172691, 0.7068273092369478], 'avgRecall': 0.7025253012048193, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T18:49:19.928074 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgAccuracy': 0.6126457831325302, 'f1': [0.552681447124797, 0.6288689014773975, 0.2770385974126309, 0.7336636489018139, 0.6573710636120903], 'avgF1': 0.569924731705746, 'precision': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgPrecision': 0.6126457831325302, 'recall': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgRecall': 0.6126457831325302, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T18:49:26.754968 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.768, 0.76, 0.7469879518072289, 0.8835341365461847, 0.6224899598393574], 'avgAccuracy': 0.7562024096385542, 'f1': [0.7701202548599418, 0.7697275576036867, 0.7532971713694605, 0.9214400791014892, 0.6375871493358014], 'avgF1': 0.7704344424540759, 'precision': [0.768, 0.76, 0.7469879518072289, 0.8835341365461847, 0.6224899598393574], 'avgPrecision': 0.7562024096385542, 'recall': [0.768, 0.76, 0.7469879518072289, 0.8835341365461847, 0.6224899598393574], 'avgRecall': 0.7562024096385542, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 100, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T18:49:27.726374 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.684, 0.752, 0.8192771084337349, 0.8554216867469879, 0.678714859437751], 'avgAccuracy': 0.7578827309236947, 'f1': [0.6889838457529963, 0.7578975069726058, 0.8240464004319425, 0.8825014706893523, 0.6936052322799305], 'avgF1': 0.7694068912253655, 'precision': [0.684, 0.752, 0.8192771084337349, 0.8554216867469879, 0.678714859437751], 'avgPrecision': 0.7578827309236947, 'recall': [0.684, 0.752, 0.8192771084337349, 0.8554216867469879, 0.678714859437751], 'avgRecall': 0.7578827309236947, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T18:49:30.187076 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.644, 0.736, 0.5983935742971888, 0.8995983935742972, 0.7068273092369478], 'avgAccuracy': 0.7169638554216867, 'f1': [0.6498693882465545, 0.7486072888025881, 0.5987695209574222, 0.9168629230017248, 0.7165262977705295], 'avgF1': 0.7261270837557638, 'precision': [0.644, 0.736, 0.5983935742971888, 0.8995983935742972, 0.7068273092369478], 'avgPrecision': 0.7169638554216867, 'recall': [0.644, 0.736, 0.5983935742971888, 0.8995983935742972, 0.7068273092369478], 'avgRecall': 0.7169638554216867, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T19:00:12.317015 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.756, 0.724, 0.5823293172690763, 0.927710843373494, 0.7630522088353414], 'avgAccuracy': 0.7506184738955823, 'f1': [0.7601932263814617, 0.7353153543545361, 0.5797858750435013, 0.9395879881549267, 0.7654463710884516], 'avgF1': 0.7560657630045755, 'precision': [0.756, 0.724, 0.5823293172690763, 0.927710843373494, 0.7630522088353414], 'avgPrecision': 0.7506184738955823, 'recall': [0.756, 0.724, 0.5823293172690763, 0.927710843373494, 0.7630522088353414], 'avgRecall': 0.7506184738955823, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.8, 0.844, 0.8554216867469879, 0.8875502008032129, 0.751004016064257], 'avgAccuracy': 0.8275951807228916, 'f1': [0.8012799525504152, 0.8450348370675508, 0.860061364742629, 0.9138940034074152, 0.7625731465438117], 'avgF1': 0.8365686608623644, 'precision': [0.8, 0.844, 0.8554216867469879, 0.8875502008032129, 0.751004016064257], 'avgPrecision': 0.8275951807228916, 'recall': [0.8, 0.844, 0.8554216867469879, 0.8875502008032129, 0.751004016064257], 'avgRecall': 0.8275951807228916, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.826811  0.835405   0.826811  0.826811   
1  0.750676  0.762282   0.750676  0.750676   
2  0.702525  0.711206   0.702525  0.702525   
3  0.612646  0.569925   0.612646  0.612646   
4  0.720090  0.731380   0.720090  0.720090   
5  0.744260  0.759423   0.744260  0.744260   
6  0.716964  0.726127   0.716964  0.716964   
7  0.725780  0.734710   0.725780  0.725780   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T19:07:43.787508 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.784, 0.86, 0.8393574297188755, 0.9317269076305221, 0.7590361445783133], 'avgAccuracy': 0.8348240963855422, 'f1': [0.7843484410157505, 0.857970794629736, 0.8446767429337789, 0.9469797597383218, 0.7642761732060102], 'avgF1': 0.8396503823047194, 'precision': [0.784, 0.86, 0.8393574297188755, 0.9317269076305221, 0.7590361445783133], 'avgPrecision': 0.8348240963855422, 'recall': [0.784, 0.86, 0.8393574297188755, 0.9317269076305221, 0.7590361445783133], 'avgRecall': 0.8348240963855422, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T19:07:52.502879 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.724, 0.764, 0.7108433734939759, 0.9236947791164659, 0.7349397590361446], 'avgAccuracy': 0.7714955823293173, 'f1': [0.7281053927508135, 0.7762844402164218, 0.7163685043203115, 0.9391090373052905, 0.7466151084881426], 'avgF1': 0.781296496616196, 'precision': [0.724, 0.764, 0.7108433734939759, 0.9236947791164659, 0.7349397590361446], 'avgPrecision': 0.7714955823293173, 'recall': [0.724, 0.764, 0.7108433734939759, 0.9236947791164659, 0.7349397590361446], 'avgRecall': 0.7714955823293173, 'params': [{'algorithm': 'kd_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T19:08:06.849187 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.64, 0.692, 0.5502008032128514, 0.8594377510040161, 0.6947791164658634], 'avgAccuracy': 0.6872835341365462, 'f1': [0.6473186036611324, 0.7025113636363638, 0.5497601110147492, 0.8946378483516264, 0.7004810265267826], 'avgF1': 0.6989417906381309, 'precision': [0.64, 0.692, 0.5502008032128514, 0.8594377510040161, 0.6947791164658634], 'avgPrecision': 0.6872835341365462, 'recall': [0.64, 0.692, 0.5502008032128514, 0.8594377510040161, 0.6947791164658634], 'avgRecall': 0.6872835341365462, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T19:08:07.253021 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgAccuracy': 0.6126457831325302, 'f1': [0.552681447124797, 0.6288689014773975, 0.2770385974126309, 0.7336636489018139, 0.6573710636120903], 'avgF1': 0.569924731705746, 'precision': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgPrecision': 0.6126457831325302, 'recall': [0.584, 0.672, 0.3493975903614458, 0.7550200803212851, 0.7028112449799196], 'avgRecall': 0.6126457831325302, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T19:08:13.910059 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.764, 0.724, 0.7590361445783133, 0.8795180722891566, 0.6144578313253012], 'avgAccuracy': 0.7482024096385542, 'f1': [0.769522288584227, 0.7384996355446899, 0.7646652118201602, 0.9116575742092858, 0.6290802902824761], 'avgF1': 0.7626850000881679, 'precision': [0.764, 0.724, 0.7590361445783133, 0.8795180722891566, 0.6144578313253012], 'avgPrecision': 0.7482024096385542, 'recall': [0.764, 0.724, 0.7590361445783133, 0.8795180722891566, 0.6144578313253012], 'avgRecall': 0.7482024096385542, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T19:08:14.794911 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.732, 0.808, 0.7951807228915663, 0.9156626506024096, 0.7389558232931727], 'avgAccuracy': 0.7979598393574298, 'f1': [0.7324002516698986, 0.8105973909131803, 0.8001785754650873, 0.9324384846797313, 0.7429773111649554], 'avgF1': 0.8037184027785705, 'precision': [0.732, 0.808, 0.7951807228915663, 0.9156626506024096, 0.7389558232931727], 'avgPrecision': 0.7979598393574298, 'recall': [0.732, 0.808, 0.7951807228915663, 0.9156626506024096, 0.7389558232931727], 'avgRecall': 0.7979598393574298, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC
2021-05-28T19:08:17.009261 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.664, 0.732, 0.5983935742971888, 0.891566265060241, 0.714859437751004], 'avgAccuracy': 0.7201638554216867, 'f1': [0.6708697781186119, 0.7444870779098284, 0.5997446014999347, 0.9115243309270528, 0.7242890191018106], 'avgF1': 0.7301829615114477, 'precision': [0.664, 0.732, 0.5983935742971888, 0.891566265060241, 0.714859437751004], 'avgPrecision': 0.7201638554216867, 'recall': [0.664, 0.732, 0.5983935742971888, 0.891566265060241, 0.714859437751004], 'avgRecall': 0.7201638554216867, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T19:19:09.600067 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.66, 0.704, 0.606425702811245, 0.9196787148594378, 0.7429718875502008], 'avgAccuracy': 0.7266152610441767, 'f1': [0.6665131805083743, 0.7179680562109805, 0.6073313413706749, 0.9350929730482624, 0.747567732684671], 'avgF1': 0.7348946567645926, 'precision': [0.66, 0.704, 0.606425702811245, 0.9196787148594378, 0.7429718875502008], 'avgPrecision': 0.7266152610441767, 'recall': [0.66, 0.704, 0.606425702811245, 0.9196787148594378, 0.7429718875502008], 'avgRecall': 0.7266152610441767, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.784, 0.86, 0.8393574297188755, 0.9317269076305221, 0.7590361445783133], 'avgAccuracy': 0.8348240963855422, 'f1': [0.7843484410157505, 0.857970794629736, 0.8446767429337789, 0.9469797597383218, 0.7642761732060102], 'avgF1': 0.8396503823047194, 'precision': [0.784, 0.86, 0.8393574297188755, 0.9317269076305221, 0.7590361445783133], 'avgPrecision': 0.8348240963855422, 'recall': [0.784, 0.86, 0.8393574297188755, 0.9317269076305221, 0.7590361445783133], 'avgRecall': 0.8348240963855422, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.831618  0.835710   0.831618  0.831618   
1  0.766673  0.778381   0.766673  0.766673   
2  0.687284  0.698942   0.687284  0.687284   
3  0.612646  0.569925   0.612646  0.612646   
4  0.742545  0.752871   0.742545  0.742545   
5  0.764263  0.774382   0.764263  0.764263   
6  0.720164  0.730183   0.720164  0.720164   
7  0.716973  0.730587   0.716973  0.716973   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T19:26:28.580124 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.788, 0.856, 0.8313253012048193, 0.9357429718875502, 0.7550200803212851], 'avgAccuracy': 0.8332176706827309, 'f1': [0.7881521194687858, 0.8534399091400158, 0.8366911024997734, 0.9492011934886881, 0.7622666243113565], 'avgF1': 0.837950189781724, 'precision': [0.788, 0.856, 0.8313253012048193, 0.9357429718875502, 0.7550200803212851], 'avgPrecision': 0.8332176706827309, 'recall': [0.788, 0.856, 0.8313253012048193, 0.9357429718875502, 0.7550200803212851], 'avgRecall': 0.8332176706827309, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T19:26:36.099547 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.728, 0.764, 0.7108433734939759, 0.9236947791164659, 0.7349397590361446], 'avgAccuracy': 0.7722955823293173, 'f1': [0.7319784367103341, 0.7762844402164218, 0.7163685043203115, 0.9391090373052905, 0.7466151084881426], 'avgF1': 0.7820711054081001, 'precision': [0.728, 0.764, 0.7108433734939759, 0.9236947791164659, 0.7349397590361446], 'avgPrecision': 0.7722955823293173, 'recall': [0.728, 0.764, 0.7108433734939759, 0.9236947791164659, 0.7349397590361446], 'avgRecall': 0.7722955823293173, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T19:26:50.419147 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.644, 0.696, 0.5502008032128514, 0.8554216867469879, 0.6867469879518072], 'avgAccuracy': 0.6864738955823293, 'f1': [0.6515173780142648, 0.7067960405848894, 0.5497601110147492, 0.892544969795147, 0.6933715527724591], 'avgF1': 0.6987980104363019, 'precision': [0.644, 0.696, 0.5502008032128514, 0.8554216867469879, 0.6867469879518072], 'avgPrecision': 0.6864738955823293, 'recall': [0.644, 0.696, 0.5502008032128514, 0.8554216867469879, 0.6867469879518072], 'avgRecall': 0.6864738955823293, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T19:26:50.890532 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.604, 0.672, 0.3453815261044177, 0.7550200803212851, 0.7108433734939759], 'avgAccuracy': 0.6174489959839358, 'f1': [0.569188315683798, 0.6294854876782174, 0.273140029577297, 0.7353835958227662, 0.664866477753949], 'avgF1': 0.5744127813032055, 'precision': [0.604, 0.672, 0.3453815261044177, 0.7550200803212851, 0.7108433734939759], 'avgPrecision': 0.6174489959839358, 'recall': [0.604, 0.672, 0.3453815261044177, 0.7550200803212851, 0.7108433734939759], 'avgRecall': 0.6174489959839358, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T19:26:57.627261 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.764, 0.724, 0.7590361445783133, 0.8795180722891566, 0.6144578313253012], 'avgAccuracy': 0.7482024096385542, 'f1': [0.769522288584227, 0.7384996355446899, 0.7646652118201602, 0.9116575742092858, 0.6290802902824761], 'avgF1': 0.7626850000881679, 'precision': [0.764, 0.724, 0.7590361445783133, 0.8795180722891566, 0.6144578313253012], 'avgPrecision': 0.7482024096385542, 'recall': [0.764, 0.724, 0.7590361445783133, 0.8795180722891566, 0.6144578313253012], 'avgRecall': 0.7482024096385542, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T19:26:58.611673 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.78, 0.8, 0.7911646586345381, 0.9236947791164659, 0.6907630522088354], 'avgAccuracy': 0.7971244979919679, 'f1': [0.7830043142043142, 0.8068975934459806, 0.7967870987114638, 0.9407879519050345, 0.7052166690720907], 'avgF1': 0.8065387254677767, 'precision': [0.78, 0.8, 0.7911646586345381, 0.9236947791164659, 0.6907630522088354], 'avgPrecision': 0.7971244979919679, 'recall': [0.78, 0.8, 0.7911646586345381, 0.9236947791164659, 0.6907630522088354], 'avgRecall': 0.7971244979919679, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T19:27:00.943352 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.668, 0.736, 0.5983935742971888, 0.891566265060241, 0.714859437751004], 'avgAccuracy': 0.7217638554216868, 'f1': [0.6747135448636591, 0.7486072888025881, 0.5997446014999347, 0.9115243309270528, 0.7242890191018106], 'avgF1': 0.7317757570390091, 'precision': [0.668, 0.736, 0.5983935742971888, 0.891566265060241, 0.714859437751004], 'avgPrecision': 0.7217638554216868, 'recall': [0.668, 0.736, 0.5983935742971888, 0.891566265060241, 0.714859437751004], 'avgRecall': 0.7217638554216868, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T19:37:39.669698 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.716, 0.704, 0.6104417670682731, 0.8674698795180723, 0.7349397590361446], 'avgAccuracy': 0.726570281124498, 'f1': [0.7215616182546077, 0.7183702818064046, 0.6118367401761848, 0.9027175436525275, 0.740901271696641], 'avgF1': 0.7390774911172732, 'precision': [0.716, 0.704, 0.6104417670682731, 0.8674698795180723, 0.7349397590361446], 'avgPrecision': 0.726570281124498, 'recall': [0.716, 0.704, 0.6104417670682731, 0.8674698795180723, 0.7349397590361446], 'avgRecall': 0.726570281124498, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.788, 0.856, 0.8313253012048193, 0.9357429718875502, 0.7550200803212851], 'avgAccuracy': 0.8332176706827309, 'f1': [0.7881521194687858, 0.8534399091400158, 0.8366911024997734, 0.9492011934886881, 0.7622666243113565], 'avgF1': 0.837950189781724, 'precision': [0.788, 0.856, 0.8313253012048193, 0.9357429718875502, 0.7550200803212851], 'avgPrecision': 0.8332176706827309, 'recall': [0.788, 0.856, 0.8313253012048193, 0.9357429718875502, 0.7550200803212851], 'avgRecall': 0.8332176706827309, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.826802  0.831627   0.826802  0.826802   
1  0.765076  0.776400   0.765076  0.765076   
2  0.686474  0.698798   0.686474  0.686474   
3  0.617449  0.574413   0.617449  0.617449   
4  0.733735  0.738241   0.733735  0.733735   
5  0.767470  0.776208   0.767470  0.767470   
6  0.721764  0.731776   0.721764  0.721764   
7  0.720945  0.732986   0.720945  0.720945   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T19:45:00.169436 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.78, 0.856, 0.8273092369477911, 0.9357429718875502, 0.7630522088353414], 'avgAccuracy': 0.8324208835341366, 'f1': [0.7798363394168718, 0.8534399091400158, 0.8326986034629208, 0.9492011934886881, 0.7693823999349707], 'avgF1': 0.8369116890886934, 'precision': [0.78, 0.856, 0.8273092369477911, 0.9357429718875502, 0.7630522088353414], 'avgPrecision': 0.8324208835341366, 'recall': [0.78, 0.856, 0.8273092369477911, 0.9357429718875502, 0.7630522088353414], 'avgRecall': 0.8324208835341366, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T19:45:07.841006 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.728, 0.764, 0.714859437751004, 0.9236947791164659, 0.7349397590361446], 'avgAccuracy': 0.7730987951807229, 'f1': [0.7319784367103341, 0.7762844402164218, 0.7204731384183625, 0.9391090373052905, 0.7466151084881426], 'avgF1': 0.7828920322277103, 'precision': [0.728, 0.764, 0.714859437751004, 0.9236947791164659, 0.7349397590361446], 'avgPrecision': 0.7730987951807229, 'recall': [0.728, 0.764, 0.714859437751004, 0.9236947791164659, 0.7349397590361446], 'avgRecall': 0.7730987951807229, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T19:45:22.603138 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.644, 0.696, 0.5502008032128514, 0.8554216867469879, 0.6867469879518072], 'avgAccuracy': 0.6864738955823293, 'f1': [0.6515173780142648, 0.7067960405848894, 0.5497601110147492, 0.892544969795147, 0.6933715527724591], 'avgF1': 0.6987980104363019, 'precision': [0.644, 0.696, 0.5502008032128514, 0.8554216867469879, 0.6867469879518072], 'avgPrecision': 0.6864738955823293, 'recall': [0.644, 0.696, 0.5502008032128514, 0.8554216867469879, 0.6867469879518072], 'avgRecall': 0.6864738955823293, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T19:45:23.009435 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.612, 0.7, 0.3453815261044177, 0.7469879518072289, 0.7108433734939759], 'avgAccuracy': 0.6230425702811244, 'f1': [0.5703163370466703, 0.6525334754751052, 0.26205392838981134, 0.7229966988372337, 0.6641128360170154], 'avgF1': 0.5744026551531672, 'precision': [0.612, 0.7, 0.3453815261044177, 0.7469879518072289, 0.7108433734939759], 'avgPrecision': 0.6230425702811244, 'recall': [0.612, 0.7, 0.3453815261044177, 0.7469879518072289, 0.7108433734939759], 'avgRecall': 0.6230425702811244, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T19:45:29.933552 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.768, 0.724, 0.7590361445783133, 0.8795180722891566, 0.6144578313253012], 'avgAccuracy': 0.7490024096385542, 'f1': [0.7745735042735044, 0.7384996355446899, 0.7646652118201602, 0.9116575742092858, 0.6290802902824761], 'avgF1': 0.7636952432260233, 'precision': [0.768, 0.724, 0.7590361445783133, 0.8795180722891566, 0.6144578313253012], 'avgPrecision': 0.7490024096385542, 'recall': [0.768, 0.724, 0.7590361445783133, 0.8795180722891566, 0.6144578313253012], 'avgRecall': 0.7490024096385542, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-28T19:45:30.958597 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.752, 0.78, 0.8072289156626506, 0.9156626506024096, 0.7068273092369478], 'avgAccuracy': 0.7923437751004017, 'f1': [0.7555122958912432, 0.7851198979631678, 0.8118047903440908, 0.9361916996929669, 0.720229847788964], 'avgF1': 0.8017717063360865, 'precision': [0.752, 0.78, 0.8072289156626506, 0.9156626506024096, 0.7068273092369478], 'avgPrecision': 0.7923437751004017, 'recall': [0.752, 0.78, 0.8072289156626506, 0.9156626506024096, 0.7068273092369478], 'avgRecall': 0.7923437751004017, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-28T19:45:33.266119 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.672, 0.736, 0.5943775100401606, 0.891566265060241, 0.714859437751004], 'avgAccuracy': 0.7217606425702812, 'f1': [0.6787383866499815, 0.7486072888025881, 0.5951309379567867, 0.9115243309270528, 0.7242890191018106], 'avgF1': 0.731657992687644, 'precision': [0.672, 0.736, 0.5943775100401606, 0.891566265060241, 0.714859437751004], 'avgPrecision': 0.7217606425702812, 'recall': [0.672, 0.736, 0.5943775100401606, 0.891566265060241, 0.714859437751004], 'avgRecall': 0.7217606425702812, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T19:54:06.594544 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.664, 0.724, 0.6104417670682731, 0.9196787148594378, 0.7469879518072289], 'avgAccuracy': 0.733021686746988, 'f1': [0.6706754885045859, 0.7385663614587823, 0.6118367401761848, 0.9350929730482624, 0.7521534710492768], 'avgF1': 0.7416650068474184, 'precision': [0.664, 0.724, 0.6104417670682731, 0.9196787148594378, 0.7469879518072289], 'avgPrecision': 0.733021686746988, 'recall': [0.664, 0.724, 0.6104417670682731, 0.9196787148594378, 0.7469879518072289], 'avgRecall': 0.733021686746988, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
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*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.78, 0.856, 0.8273092369477911, 0.9357429718875502, 0.7630522088353414], 'avgAccuracy': 0.8324208835341366, 'f1': [0.7798363394168718, 0.8534399091400158, 0.8326986034629208, 0.9492011934886881, 0.7693823999349707], 'avgF1': 0.8369116890886934, 'precision': [0.78, 0.856, 0.8273092369477911, 0.9357429718875502, 0.7630522088353414], 'avgPrecision': 0.8324208835341366, 'recall': [0.78, 0.856, 0.8273092369477911, 0.9357429718875502, 0.7630522088353414], 'avgRecall': 0.8324208835341366, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.830008  0.834708   0.830008  0.830008   
1  0.765880  0.777115   0.765880  0.765880   
2  0.686474  0.698798   0.686474  0.686474   
3  0.623043  0.574403   0.623043  0.623043   
4  0.729729  0.736390   0.729729  0.729729   
5  0.789934  0.796281   0.789934  0.789934   
6  0.721761  0.731658   0.721761  0.721761   
7  0.710545  0.722100   0.710545  0.710545   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T20:02:02.716530 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
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* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.788, 0.856, 0.8232931726907631, 0.9357429718875502, 0.7630522088353414], 'avgAccuracy': 0.8332176706827309, 'f1': [0.786629708950831, 0.8543432502456894, 0.8287055946342051, 0.9494220853915963, 0.7693823999349707], 'avgF1': 0.8376966078314585, 'precision': [0.788, 0.856, 0.8232931726907631, 0.9357429718875502, 0.7630522088353414], 'avgPrecision': 0.8332176706827309, 'recall': [0.788, 0.856, 0.8232931726907631, 0.9357429718875502, 0.7630522088353414], 'avgRecall': 0.8332176706827309, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-28T20:02:10.679314 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.728, 0.76, 0.714859437751004, 0.9196787148594378, 0.7389558232931727], 'avgAccuracy': 0.7722987951807229, 'f1': [0.7319784367103341, 0.7726407690445728, 0.7199124962738154, 0.9368622880705895, 0.750468962288955], 'avgF1': 0.7823725904776534, 'precision': [0.728, 0.76, 0.714859437751004, 0.9196787148594378, 0.7389558232931727], 'avgPrecision': 0.7722987951807229, 'recall': [0.728, 0.76, 0.714859437751004, 0.9196787148594378, 0.7389558232931727], 'avgRecall': 0.7722987951807229, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
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Processing Model: LogisticRegression
2021-05-28T20:02:25.701664 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.644, 0.692, 0.5542168674698795, 0.8554216867469879, 0.6867469879518072], 'avgAccuracy': 0.6864771084337349, 'f1': [0.6515173780142648, 0.7031942175471588, 0.5542438542853997, 0.892544969795147, 0.6925141788070583], 'avgF1': 0.6988029196898057, 'precision': [0.644, 0.692, 0.5542168674698795, 0.8554216867469879, 0.6867469879518072], 'avgPrecision': 0.6864771084337349, 'recall': [0.644, 0.692, 0.5542168674698795, 0.8554216867469879, 0.6867469879518072], 'avgRecall': 0.6864771084337349, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T20:02:26.092295 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.612, 0.7, 0.3453815261044177, 0.7469879518072289, 0.7108433734939759], 'avgAccuracy': 0.6230425702811244, 'f1': [0.5703163370466703, 0.6525334754751052, 0.26205392838981134, 0.7229966988372337, 0.6641128360170154], 'avgF1': 0.5744026551531672, 'precision': [0.612, 0.7, 0.3453815261044177, 0.7469879518072289, 0.7108433734939759], 'avgPrecision': 0.6230425702811244, 'recall': [0.612, 0.7, 0.3453815261044177, 0.7469879518072289, 0.7108433734939759], 'avgRecall': 0.6230425702811244, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-28T20:02:34.348592 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.748, 0.788, 0.751004016064257, 0.8714859437751004, 0.5220883534136547], 'avgAccuracy': 0.7361156626506025, 'f1': [0.7539368421052631, 0.7920261861464706, 0.7564118716678138, 0.9065167298837837, 0.5106325720662253], 'avgF1': 0.7439048403739112, 'precision': [0.748, 0.788, 0.751004016064257, 0.8714859437751004, 0.5220883534136547], 'avgPrecision': 0.7361156626506025, 'recall': [0.748, 0.788, 0.751004016064257, 0.8714859437751004, 0.5220883534136547], 'avgRecall': 0.7361156626506025, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-28T20:02:35.476833 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.764, 0.792, 0.7911646586345381, 0.9116465863453815, 0.7108433734939759], 'avgAccuracy': 0.7939309236947791, 'f1': [0.76549198056337, 0.7921286327005559, 0.7965508189093156, 0.9368990767271568, 0.7175936332122722], 'avgF1': 0.8017328284225341, 'precision': [0.764, 0.792, 0.7911646586345381, 0.9116465863453815, 0.7108433734939759], 'avgPrecision': 0.7939309236947791, 'recall': [0.764, 0.792, 0.7911646586345381, 0.9116465863453815, 0.7108433734939759], 'avgRecall': 0.7939309236947791, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-28T20:02:38.017548 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.66, 0.728, 0.5301204819277109, 0.8875502008032129, 0.714859437751004], 'avgAccuracy': 0.7041060240963856, 'f1': [0.6666002938348524, 0.741433586337761, 0.5152696686702893, 0.9087924732638418, 0.7242890191018106], 'avgF1': 0.711277008241711, 'precision': [0.66, 0.728, 0.5301204819277109, 0.8875502008032129, 0.714859437751004], 'avgPrecision': 0.7041060240963856, 'recall': [0.66, 0.728, 0.5301204819277109, 0.8875502008032129, 0.714859437751004], 'avgRecall': 0.7041060240963856, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T20:13:07.763698 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.66, 0.712, 0.606425702811245, 0.9196787148594378, 0.7469879518072289], 'avgAccuracy': 0.7290184738955823, 'f1': [0.6666002938348524, 0.7260351575007857, 0.6073313413706749, 0.9350929730482624, 0.7513046890790139], 'avgF1': 0.7372728909667179, 'precision': [0.66, 0.712, 0.606425702811245, 0.9196787148594378, 0.7469879518072289], 'avgPrecision': 0.7290184738955823, 'recall': [0.66, 0.712, 0.606425702811245, 0.9196787148594378, 0.7469879518072289], 'avgRecall': 0.7290184738955823, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.788, 0.856, 0.8232931726907631, 0.9357429718875502, 0.7630522088353414], 'avgAccuracy': 0.8332176706827309, 'f1': [0.786629708950831, 0.8543432502456894, 0.8287055946342051, 0.9494220853915963, 0.7693823999349707], 'avgF1': 0.8376966078314585, 'precision': [0.788, 0.856, 0.8232931726907631, 0.9357429718875502, 0.7630522088353414], 'avgPrecision': 0.8332176706827309, 'recall': [0.788, 0.856, 0.8232931726907631, 0.9357429718875502, 0.7630522088353414], 'avgRecall': 0.8332176706827309, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.830011  0.834631   0.830011  0.830011   
1  0.763473  0.775215   0.763473  0.763473   
2  0.686477  0.698803   0.686477  0.686477   
3  0.623043  0.574403   0.623043  0.623043   
4  0.732132  0.742984   0.732132  0.732132   
5  0.770683  0.780004   0.770683  0.770683   
6  0.704106  0.711277   0.704106  0.704106   
7  0.714567  0.728096   0.714567  0.714567   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T20:20:29.039371 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.78, 0.856, 0.8353413654618473, 0.9357429718875502, 0.7469879518072289], 'avgAccuracy': 0.8308144578313252, 'f1': [0.7795010339122027, 0.8524858181818181, 0.8414200373539021, 0.9454091437382768, 0.7544001310885018], 'avgF1': 0.8346432328549404, 'precision': [0.78, 0.856, 0.8353413654618473, 0.9357429718875502, 0.7469879518072289], 'avgPrecision': 0.8308144578313252, 'recall': [0.78, 0.856, 0.8353413654618473, 0.9357429718875502, 0.7469879518072289], 'avgRecall': 0.8308144578313252, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T20:20:36.738588 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.736, 0.772, 0.714859437751004, 0.9116465863453815, 0.7389558232931727], 'avgAccuracy': 0.7746923694779116, 'f1': [0.7397384221916191, 0.7827992743398509, 0.7207223088002178, 0.9318778462896489, 0.750468962288955], 'avgF1': 0.7851213627820584, 'precision': [0.736, 0.772, 0.714859437751004, 0.9116465863453815, 0.7389558232931727], 'avgPrecision': 0.7746923694779116, 'recall': [0.736, 0.772, 0.714859437751004, 0.9116465863453815, 0.7389558232931727], 'avgRecall': 0.7746923694779116, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T20:20:51.450745 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.644, 0.696, 0.5542168674698795, 0.8554216867469879, 0.6827309236947792], 'avgAccuracy': 0.6864738955823293, 'f1': [0.6515173780142648, 0.7070664392236676, 0.5547561522211567, 0.892544969795147, 0.6895943343464702], 'avgF1': 0.6990958547201412, 'precision': [0.644, 0.696, 0.5542168674698795, 0.8554216867469879, 0.6827309236947792], 'avgPrecision': 0.6864738955823293, 'recall': [0.644, 0.696, 0.5542168674698795, 0.8554216867469879, 0.6827309236947792], 'avgRecall': 0.6864738955823293, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T20:20:51.888247 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.616, 0.7, 0.3493975903614458, 0.7429718875502008, 0.6987951807228916], 'avgAccuracy': 0.6214329317269076, 'f1': [0.5743860234578201, 0.6533296173711151, 0.26429635828605347, 0.7209942130727635, 0.6533293668501166], 'avgF1': 0.5732671158075737, 'precision': [0.616, 0.7, 0.3493975903614458, 0.7429718875502008, 0.6987951807228916], 'avgPrecision': 0.6214329317269076, 'recall': [0.616, 0.7, 0.3493975903614458, 0.7429718875502008, 0.6987951807228916], 'avgRecall': 0.6214329317269076, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T20:20:58.941918 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.736, 0.752, 0.7710843373493976, 0.8674698795180723, 0.6144578313253012], 'avgAccuracy': 0.7482024096385542, 'f1': [0.7415755725190839, 0.7651572536716337, 0.7774378031904204, 0.8997312209418317, 0.6358992095780461], 'avgF1': 0.7639602119802031, 'precision': [0.736, 0.752, 0.7710843373493976, 0.8674698795180723, 0.6144578313253012], 'avgPrecision': 0.7482024096385542, 'recall': [0.736, 0.752, 0.7710843373493976, 0.8674698795180723, 0.6144578313253012], 'avgRecall': 0.7482024096385542, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 100, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T20:20:59.961100 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.732, 0.828, 0.7751004016064257, 0.9236947791164659, 0.6947791164658634], 'avgAccuracy': 0.790714859437751, 'f1': [0.7319724058583545, 0.825237214280254, 0.7848948414098742, 0.9402637476937555, 0.7067286569820068], 'avgF1': 0.797819373244849, 'precision': [0.732, 0.828, 0.7751004016064257, 0.9236947791164659, 0.6947791164658634], 'avgPrecision': 0.790714859437751, 'recall': [0.732, 0.828, 0.7751004016064257, 0.9236947791164659, 0.6947791164658634], 'avgRecall': 0.790714859437751, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T20:21:02.195187 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.656, 0.732, 0.606425702811245, 0.8875502008032129, 0.714859437751004], 'avgAccuracy': 0.7193670682730924, 'f1': [0.6626529299037235, 0.7436796767676769, 0.6068806541846241, 0.9087924732638418, 0.7242890191018106], 'avgF1': 0.7292589506443354, 'precision': [0.656, 0.732, 0.606425702811245, 0.8875502008032129, 0.714859437751004], 'avgPrecision': 0.7193670682730924, 'recall': [0.656, 0.732, 0.606425702811245, 0.8875502008032129, 0.714859437751004], 'avgRecall': 0.7193670682730924, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T20:30:48.067710 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.712, 0.716, 0.606425702811245, 0.8674698795180723, 0.7469879518072289], 'avgAccuracy': 0.7297767068273092, 'f1': [0.7178415971404832, 0.7287226890756303, 0.607451731342416, 0.9006623631062487, 0.7525408726831597], 'avgF1': 0.7414438506695876, 'precision': [0.712, 0.716, 0.606425702811245, 0.8674698795180723, 0.7469879518072289], 'avgPrecision': 0.7297767068273092, 'recall': [0.712, 0.716, 0.606425702811245, 0.8674698795180723, 0.7469879518072289], 'avgRecall': 0.7297767068273092, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.78, 0.856, 0.8353413654618473, 0.9357429718875502, 0.7469879518072289], 'avgAccuracy': 0.8308144578313252, 'f1': [0.7795010339122027, 0.8524858181818181, 0.8414200373539021, 0.9454091437382768, 0.7544001310885018], 'avgF1': 0.8346432328549404, 'precision': [0.78, 0.856, 0.8353413654618473, 0.9357429718875502, 0.7469879518072289], 'avgPrecision': 0.8308144578313252, 'recall': [0.78, 0.856, 0.8353413654618473, 0.9357429718875502, 0.7469879518072289], 'avgRecall': 0.8308144578313252, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.827602  0.831613   0.827602  0.827602   
1  0.765876  0.777462   0.765876  0.765876   
2  0.686474  0.699096   0.686474  0.686474   
3  0.621433  0.573267   0.621433  0.621433   
4  0.743438  0.758760   0.743438  0.743438   
5  0.766673  0.776639   0.766673  0.766673   
6  0.719367  0.729259   0.719367  0.719367   
7  0.713754  0.726331   0.713754  0.713754   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T20:38:08.964714 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.772, 0.836, 0.8353413654618473, 0.9236947791164659, 0.7309236947791165], 'avgAccuracy': 0.8195919678714859, 'f1': [0.772909546119789, 0.8309398482280999, 0.841596589315639, 0.9388218244517653, 0.7349564267916341], 'avgF1': 0.8238448469813855, 'precision': [0.772, 0.836, 0.8353413654618473, 0.9236947791164659, 0.7309236947791165], 'avgPrecision': 0.8195919678714859, 'recall': [0.772, 0.836, 0.8353413654618473, 0.9236947791164659, 0.7309236947791165], 'avgRecall': 0.8195919678714859, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T20:38:16.839828 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.7, 0.772, 0.7228915662650602, 0.8955823293172691, 0.7228915662650602], 'avgAccuracy': 0.7626730923694779, 'f1': [0.705693483980808, 0.7783788706739526, 0.728489924360571, 0.9266940288627036, 0.7320795177935968], 'avgF1': 0.7742671651343264, 'precision': [0.7, 0.772, 0.7228915662650602, 0.8955823293172691, 0.7228915662650602], 'avgPrecision': 0.7626730923694779, 'recall': [0.7, 0.772, 0.7228915662650602, 0.8955823293172691, 0.7228915662650602], 'avgRecall': 0.7626730923694779, 'params': [{'algorithm': 'kd_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T20:38:31.242996 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.644, 0.708, 0.4979919678714859, 0.8714859437751004, 0.6947791164658634], 'avgAccuracy': 0.68325140562249, 'f1': [0.6529822888447414, 0.7178517553726835, 0.4695538886056692, 0.9009413968269467, 0.7022277978832643], 'avgF1': 0.6887114255066611, 'precision': [0.644, 0.708, 0.4979919678714859, 0.8714859437751004, 0.6947791164658634], 'avgPrecision': 0.68325140562249, 'recall': [0.644, 0.708, 0.4979919678714859, 0.8714859437751004, 0.6947791164658634], 'avgRecall': 0.68325140562249, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T20:38:31.633276 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.616, 0.7, 0.3493975903614458, 0.7429718875502008, 0.6987951807228916], 'avgAccuracy': 0.6214329317269076, 'f1': [0.5743860234578201, 0.6533296173711151, 0.26429635828605347, 0.7209942130727635, 0.6533293668501166], 'avgF1': 0.5732671158075737, 'precision': [0.616, 0.7, 0.3493975903614458, 0.7429718875502008, 0.6987951807228916], 'avgPrecision': 0.6214329317269076, 'recall': [0.616, 0.7, 0.3493975903614458, 0.7429718875502008, 0.6987951807228916], 'avgRecall': 0.6214329317269076, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-28T20:38:38.462171 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.72, 0.804, 0.7429718875502008, 0.8473895582329317, 0.6666666666666666], 'avgAccuracy': 0.7562056224899598, 'f1': [0.7251825853682078, 0.8088976554656587, 0.7432984685996734, 0.8884841692350799, 0.6849408214870063], 'avgF1': 0.7701607400311252, 'precision': [0.72, 0.804, 0.7429718875502008, 0.8473895582329317, 0.6666666666666666], 'avgPrecision': 0.7562056224899598, 'recall': [0.72, 0.804, 0.7429718875502008, 0.8473895582329317, 0.6666666666666666], 'avgRecall': 0.7562056224899598, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T20:38:39.452619 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.764, 0.788, 0.7751004016064257, 0.9076305220883534, 0.6827309236947792], 'avgAccuracy': 0.7834923694779117, 'f1': [0.7697912741706436, 0.790306127152758, 0.7814546785067165, 0.9314622227791003, 0.6925451638882828], 'avgF1': 0.7931118932995003, 'precision': [0.764, 0.788, 0.7751004016064257, 0.9076305220883534, 0.6827309236947792], 'avgPrecision': 0.7834923694779117, 'recall': [0.764, 0.788, 0.7751004016064257, 0.9076305220883534, 0.6827309236947792], 'avgRecall': 0.7834923694779117, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-28T20:38:41.600832 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.656, 0.716, 0.5341365461847389, 0.8795180722891566, 0.714859437751004], 'avgAccuracy': 0.7001028112449799, 'f1': [0.6626529299037235, 0.7293018597095743, 0.5171454232403913, 0.903198669477267, 0.7247487471217662], 'avgF1': 0.7074095258905444, 'precision': [0.656, 0.716, 0.5341365461847389, 0.8795180722891566, 0.714859437751004], 'avgPrecision': 0.7001028112449799, 'recall': [0.656, 0.716, 0.5341365461847389, 0.8795180722891566, 0.714859437751004], 'avgRecall': 0.7001028112449799, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T20:49:35.401042 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.712, 0.72, 0.4899598393574297, 0.9036144578313253, 0.7028112449799196], 'avgAccuracy': 0.7056771084337349, 'f1': [0.7183457794491985, 0.7332605042016808, 0.46327499431105224, 0.925305419579367, 0.713113943837614], 'avgF1': 0.7106601282757825, 'precision': [0.712, 0.72, 0.4899598393574297, 0.9036144578313253, 0.7028112449799196], 'avgPrecision': 0.7056771084337349, 'recall': [0.712, 0.72, 0.4899598393574297, 0.9036144578313253, 0.7028112449799196], 'avgRecall': 0.7056771084337349, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.772, 0.836, 0.8353413654618473, 0.9236947791164659, 0.7309236947791165], 'avgAccuracy': 0.8195919678714859, 'f1': [0.772909546119789, 0.8309398482280999, 0.841596589315639, 0.9388218244517653, 0.7349564267916341], 'avgF1': 0.8238448469813855, 'precision': [0.772, 0.836, 0.8353413654618473, 0.9236947791164659, 0.7309236947791165], 'avgPrecision': 0.8195919678714859, 'recall': [0.772, 0.836, 0.8353413654618473, 0.9236947791164659, 0.7309236947791165], 'avgRecall': 0.8195919678714859, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.813179  0.817929   0.813179  0.813179   
1  0.757870  0.770136   0.757870  0.757870   
2  0.683251  0.688711   0.683251  0.683251   
3  0.621433  0.573267   0.621433  0.621433   
4  0.756206  0.770161   0.756206  0.756206   
5  0.763460  0.773140   0.763460  0.763460   
6  0.700103  0.707410   0.700103  0.700103   
7  0.700067  0.707921   0.700067  0.700067   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T20:56:53.411525 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.776, 0.84, 0.8192771084337349, 0.927710843373494, 0.7389558232931727], 'avgAccuracy': 0.8203887550200804, 'f1': [0.7766664971392244, 0.8339628402826885, 0.8254627231186004, 0.9410717381220082, 0.7429914531976394], 'avgF1': 0.8240310503720322, 'precision': [0.776, 0.84, 0.8192771084337349, 0.927710843373494, 0.7389558232931727], 'avgPrecision': 0.8203887550200804, 'recall': [0.776, 0.84, 0.8192771084337349, 0.927710843373494, 0.7389558232931727], 'avgRecall': 0.8203887550200804, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-28T20:57:00.928701 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.7, 0.784, 0.7389558232931727, 0.8955823293172691, 0.714859437751004], 'avgAccuracy': 0.7666795180722892, 'f1': [0.7064394569471624, 0.7911990988223246, 0.7447150953176057, 0.9261677998018709, 0.7245687598029042], 'avgF1': 0.7786180421383736, 'precision': [0.7, 0.784, 0.7389558232931727, 0.8955823293172691, 0.714859437751004], 'avgPrecision': 0.7666795180722892, 'recall': [0.7, 0.784, 0.7389558232931727, 0.8955823293172691, 0.714859437751004], 'avgRecall': 0.7666795180722892, 'params': [{'algorithm': 'kd_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
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Processing Model: LogisticRegression
2021-05-28T20:57:15.694238 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.64, 0.716, 0.4979919678714859, 0.8714859437751004, 0.6947791164658634], 'avgAccuracy': 0.6840514056224899, 'f1': [0.648939384083606, 0.7264462935725085, 0.4695538886056692, 0.9009413968269467, 0.7013312066920999], 'avgF1': 0.689442433956166, 'precision': [0.64, 0.716, 0.4979919678714859, 0.8714859437751004, 0.6947791164658634], 'avgPrecision': 0.6840514056224899, 'recall': [0.64, 0.716, 0.4979919678714859, 0.8714859437751004, 0.6947791164658634], 'avgRecall': 0.6840514056224899, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T20:57:16.147366 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.616, 0.704, 0.3534136546184739, 0.7429718875502008, 0.7108433734939759], 'avgAccuracy': 0.6254457831325301, 'f1': [0.5743860234578201, 0.6566827155717494, 0.26652222321870095, 0.7209942130727635, 0.6690525732634606], 'avgF1': 0.5775275497168989, 'precision': [0.616, 0.704, 0.3534136546184739, 0.7429718875502008, 0.7108433734939759], 'avgPrecision': 0.6254457831325301, 'recall': [0.616, 0.704, 0.3534136546184739, 0.7429718875502008, 0.7108433734939759], 'avgRecall': 0.6254457831325301, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T20:57:23.252976 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.7, 0.772, 0.714859437751004, 0.8995983935742972, 0.6666666666666666], 'avgAccuracy': 0.7506248995983935, 'f1': [0.6979960785782974, 0.7765887300252313, 0.7201478275748696, 0.922138603546186, 0.6849408214870063], 'avgF1': 0.7603624122423182, 'precision': [0.7, 0.772, 0.714859437751004, 0.8995983935742972, 0.6666666666666666], 'avgPrecision': 0.7506248995983935, 'recall': [0.7, 0.772, 0.714859437751004, 0.8995983935742972, 0.6666666666666666], 'avgRecall': 0.7506248995983935, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T20:57:24.230461 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.7, 0.772, 0.7951807228915663, 0.9236947791164659, 0.7389558232931727], 'avgAccuracy': 0.7859662650602409, 'f1': [0.7059734560527223, 0.7746715881272951, 0.8011899529140561, 0.9422307150309717, 0.734135535899494], 'avgF1': 0.7916402496049079, 'precision': [0.7, 0.772, 0.7951807228915663, 0.9236947791164659, 0.7389558232931727], 'avgPrecision': 0.7859662650602409, 'recall': [0.7, 0.772, 0.7951807228915663, 0.9236947791164659, 0.7389558232931727], 'avgRecall': 0.7859662650602409, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC
2021-05-28T20:57:26.389352 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.648, 0.716, 0.5301204819277109, 0.8795180722891566, 0.7108433734939759], 'avgAccuracy': 0.6968963855421687, 'f1': [0.6545388965541685, 0.729734234452394, 0.513846338692965, 0.903198669477267, 0.7210744599054304], 'avgF1': 0.704478519816445, 'precision': [0.648, 0.716, 0.5301204819277109, 0.8795180722891566, 0.7108433734939759], 'avgPrecision': 0.6968963855421687, 'recall': [0.648, 0.716, 0.5301204819277109, 0.8795180722891566, 0.7108433734939759], 'avgRecall': 0.6968963855421687, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T21:05:29.434754 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.712, 0.732, 0.4859437751004016, 0.8755020080321285, 0.6987951807228916], 'avgAccuracy': 0.7008481927710843, 'f1': [0.718876623260294, 0.7450548963186864, 0.46009481602854535, 0.902088236963803, 0.7078926274569298], 'avgF1': 0.7068014400056517, 'precision': [0.712, 0.732, 0.4859437751004016, 0.8755020080321285, 0.6987951807228916], 'avgPrecision': 0.7008481927710843, 'recall': [0.712, 0.732, 0.4859437751004016, 0.8755020080321285, 0.6987951807228916], 'avgRecall': 0.7008481927710843, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.776, 0.84, 0.8192771084337349, 0.927710843373494, 0.7389558232931727], 'avgAccuracy': 0.8203887550200804, 'f1': [0.7766664971392244, 0.8339628402826885, 0.8254627231186004, 0.9410717381220082, 0.7429914531976394], 'avgF1': 0.8240310503720322, 'precision': [0.776, 0.84, 0.8192771084337349, 0.927710843373494, 0.7389558232931727], 'avgPrecision': 0.8203887550200804, 'recall': [0.776, 0.84, 0.8192771084337349, 0.927710843373494, 0.7389558232931727], 'avgRecall': 0.8203887550200804, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.813979  0.817475   0.813979  0.813979   
1  0.759467  0.772137   0.759467  0.759467   
2  0.684051  0.689442   0.684051  0.684051   
3  0.625446  0.577528   0.625446  0.625446   
4  0.750625  0.760362   0.750625  0.750625   
5  0.773131  0.783872   0.773131  0.773131   
6  0.696896  0.704479   0.696896  0.696896   
7  0.692058  0.698736   0.692058  0.692058   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T21:13:19.962476 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.78, 0.836, 0.8313253012048193, 0.9236947791164659, 0.7389558232931727], 'avgAccuracy': 0.8219951807228916, 'f1': [0.7804267558528427, 0.830808625365094, 0.8375626522919922, 0.9388218244517653, 0.7429914531976394], 'avgF1': 0.8261222622318667, 'precision': [0.78, 0.836, 0.8313253012048193, 0.9236947791164659, 0.7389558232931727], 'avgPrecision': 0.8219951807228916, 'recall': [0.78, 0.836, 0.8313253012048193, 0.9236947791164659, 0.7389558232931727], 'avgRecall': 0.8219951807228916, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-28T21:13:29.941578 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.684, 0.784, 0.7309236947791165, 0.8955823293172691, 0.7469879518072289], 'avgAccuracy': 0.7682987951807229, 'f1': [0.6922471857532823, 0.7933199999999999, 0.7371299387789383, 0.9266940288627036, 0.7535639477453877], 'avgF1': 0.7805910202280624, 'precision': [0.684, 0.784, 0.7309236947791165, 0.8955823293172691, 0.7469879518072289], 'avgPrecision': 0.7682987951807229, 'recall': [0.684, 0.784, 0.7309236947791165, 0.8955823293172691, 0.7469879518072289], 'avgRecall': 0.7682987951807229, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T21:13:49.775203 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.64, 0.716, 0.4979919678714859, 0.8714859437751004, 0.6947791164658634], 'avgAccuracy': 0.6840514056224899, 'f1': [0.648939384083606, 0.7264462935725085, 0.4695538886056692, 0.9009413968269467, 0.7013312066920999], 'avgF1': 0.689442433956166, 'precision': [0.64, 0.716, 0.4979919678714859, 0.8714859437751004, 0.6947791164658634], 'avgPrecision': 0.6840514056224899, 'recall': [0.64, 0.716, 0.4979919678714859, 0.8714859437751004, 0.6947791164658634], 'avgRecall': 0.6840514056224899, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T21:13:50.275204 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.58, 0.652, 0.42971887550200805, 0.8192771084337349, 0.6666666666666666], 'avgAccuracy': 0.6295325301204819, 'f1': [0.5855474178403756, 0.643024990250421, 0.4360372748608907, 0.8170616511418715, 0.6570389736403893], 'avgF1': 0.6277420615467896, 'precision': [0.58, 0.652, 0.42971887550200805, 0.8192771084337349, 0.6666666666666666], 'avgPrecision': 0.6295325301204819, 'recall': [0.58, 0.652, 0.42971887550200805, 0.8192771084337349, 0.6666666666666666], 'avgRecall': 0.6295325301204819, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T21:14:00.221374 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.7, 0.772, 0.714859437751004, 0.8995983935742972, 0.6666666666666666], 'avgAccuracy': 0.7506248995983935, 'f1': [0.6979960785782974, 0.7765887300252313, 0.7201478275748696, 0.922138603546186, 0.6849408214870063], 'avgF1': 0.7603624122423182, 'precision': [0.7, 0.772, 0.714859437751004, 0.8995983935742972, 0.6666666666666666], 'avgPrecision': 0.7506248995983935, 'recall': [0.7, 0.772, 0.714859437751004, 0.8995983935742972, 0.6666666666666666], 'avgRecall': 0.7506248995983935, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T21:14:01.591067 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.716, 0.792, 0.7911646586345381, 0.9236947791164659, 0.6827309236947792], 'avgAccuracy': 0.7811180722891566, 'f1': [0.721791924945552, 0.7967000000000001, 0.7967056267485175, 0.9401470616766582, 0.6938099904745433], 'avgF1': 0.7898309207690543, 'precision': [0.716, 0.792, 0.7911646586345381, 0.9236947791164659, 0.6827309236947792], 'avgPrecision': 0.7811180722891566, 'recall': [0.716, 0.792, 0.7911646586345381, 0.9236947791164659, 0.6827309236947792], 'avgRecall': 0.7811180722891566, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
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Processing Model: SVC
2021-05-28T21:14:04.695148 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.648, 0.716, 0.5622489959839357, 0.8795180722891566, 0.7188755020080321], 'avgAccuracy': 0.7049285140562249, 'f1': [0.6545388965541685, 0.729734234452394, 0.5589564007864332, 0.903198669477267, 0.7284075789015669], 'avgF1': 0.7149671560343659, 'precision': [0.648, 0.716, 0.5622489959839357, 0.8795180722891566, 0.7188755020080321], 'avgPrecision': 0.7049285140562249, 'recall': [0.648, 0.716, 0.5622489959839357, 0.8795180722891566, 0.7188755020080321], 'avgRecall': 0.7049285140562249, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T21:22:25.282223 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.66, 0.728, 0.5220883534136547, 0.8755020080321285, 0.714859437751004], 'avgAccuracy': 0.7000899598393574, 'f1': [0.6668460724717772, 0.7414096220654806, 0.5208909728881915, 0.902088236963803, 0.723313342844747], 'avgF1': 0.7109096494467999, 'precision': [0.66, 0.728, 0.5220883534136547, 0.8755020080321285, 0.714859437751004], 'avgPrecision': 0.7000899598393574, 'recall': [0.66, 0.728, 0.5220883534136547, 0.8755020080321285, 0.714859437751004], 'avgRecall': 0.7000899598393574, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.78, 0.836, 0.8313253012048193, 0.9236947791164659, 0.7389558232931727], 'avgAccuracy': 0.8219951807228916, 'f1': [0.7804267558528427, 0.830808625365094, 0.8375626522919922, 0.9388218244517653, 0.7429914531976394], 'avgF1': 0.8261222622318667, 'precision': [0.78, 0.836, 0.8313253012048193, 0.9236947791164659, 0.7389558232931727], 'avgPrecision': 0.8219951807228916, 'recall': [0.78, 0.836, 0.8313253012048193, 0.9236947791164659, 0.7389558232931727], 'avgRecall': 0.8219951807228916, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.817976  0.822467   0.817976  0.817976   
1  0.761876  0.774752   0.761876  0.761876   
2  0.684051  0.689442   0.684051  0.684051   
3  0.629533  0.627742   0.629533  0.629533   
4  0.750625  0.760362   0.750625  0.750625   
5  0.781118  0.789831   0.781118  0.781118   
6  0.704929  0.714967   0.704929  0.704929   
7  0.695271  0.703731   0.695271  0.695271   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T21:29:44.618061 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.792, 0.84, 0.8232931726907631, 0.927710843373494, 0.7269076305220884], 'avgAccuracy': 0.8219823293172691, 'f1': [0.7933593897012079, 0.8352161616161616, 0.8294962720494635, 0.9430378655752774, 0.7313628679313001], 'avgF1': 0.8264945113746821, 'precision': [0.792, 0.84, 0.8232931726907631, 0.927710843373494, 0.7269076305220884], 'avgPrecision': 0.8219823293172691, 'recall': [0.792, 0.84, 0.8232931726907631, 0.927710843373494, 0.7269076305220884], 'avgRecall': 0.8219823293172691, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T21:29:52.473469 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.688, 0.764, 0.7228915662650602, 0.8995983935742972, 0.7429718875502008], 'avgAccuracy': 0.7634923694779117, 'f1': [0.6961808273027784, 0.775695450081833, 0.7291491752219664, 0.9292748748538319, 0.7501510956083254], 'avgF1': 0.7760902846137471, 'precision': [0.688, 0.764, 0.7228915662650602, 0.8995983935742972, 0.7429718875502008], 'avgPrecision': 0.7634923694779117, 'recall': [0.688, 0.764, 0.7228915662650602, 0.8995983935742972, 0.7429718875502008], 'avgRecall': 0.7634923694779117, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
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Processing Model: LogisticRegression
2021-05-28T21:30:06.904644 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.636, 0.72, 0.5020080321285141, 0.8714859437751004, 0.6987951807228916], 'avgAccuracy': 0.6856578313253012, 'f1': [0.6445309169594243, 0.7307065560688479, 0.4752684600772605, 0.9009413968269467, 0.7049089596044538], 'avgF1': 0.6912712579073866, 'precision': [0.636, 0.72, 0.5020080321285141, 0.8714859437751004, 0.6987951807228916], 'avgPrecision': 0.6856578313253012, 'recall': [0.636, 0.72, 0.5020080321285141, 0.8714859437751004, 0.6987951807228916], 'avgRecall': 0.6856578313253012, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T21:30:07.357724 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.576, 0.652, 0.42570281124497994, 0.8232931726907631, 0.6706827309236948], 'avgAccuracy': 0.6295357429718875, 'f1': [0.581474614572588, 0.643024990250421, 0.42724419809771724, 0.8228878361744192, 0.6595610572650807], 'avgF1': 0.6268385392720452, 'precision': [0.576, 0.652, 0.42570281124497994, 0.8232931726907631, 0.6706827309236948], 'avgPrecision': 0.6295357429718875, 'recall': [0.576, 0.652, 0.42570281124497994, 0.8232931726907631, 0.6706827309236948], 'avgRecall': 0.6295357429718875, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-28T21:30:15.401366 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.7, 0.784, 0.714859437751004, 0.8594377510040161, 0.6666666666666666], 'avgAccuracy': 0.7449927710843374, 'f1': [0.6979960785782974, 0.7845305605341651, 0.7201478275748696, 0.8968389413892356, 0.6849408214870063], 'avgF1': 0.7568908459127148, 'precision': [0.7, 0.784, 0.714859437751004, 0.8594377510040161, 0.6666666666666666], 'avgPrecision': 0.7449927710843374, 'recall': [0.7, 0.784, 0.714859437751004, 0.8594377510040161, 0.6666666666666666], 'avgRecall': 0.7449927710843374, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-28T21:30:16.387492 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.768, 0.792, 0.7831325301204819, 0.9156626506024096, 0.6706827309236948], 'avgAccuracy': 0.7858955823293172, 'f1': [0.7740820679993241, 0.7966746689066658, 0.7897327162735878, 0.9345838944804701, 0.682125471866087], 'avgF1': 0.795439763905227, 'precision': [0.768, 0.792, 0.7831325301204819, 0.9156626506024096, 0.6706827309236948], 'avgPrecision': 0.7858955823293172, 'recall': [0.768, 0.792, 0.7831325301204819, 0.9156626506024096, 0.6706827309236948], 'avgRecall': 0.7858955823293172, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-28T21:30:18.766274 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.656, 0.72, 0.5662650602409639, 0.8795180722891566, 0.7188755020080321], 'avgAccuracy': 0.7081317269076305, 'f1': [0.6626529299037235, 0.732912, 0.563652459299564, 0.903198669477267, 0.7284075789015669], 'avgF1': 0.7181647275164242, 'precision': [0.656, 0.72, 0.5662650602409639, 0.8795180722891566, 0.7188755020080321], 'avgPrecision': 0.7081317269076305, 'recall': [0.656, 0.72, 0.5662650602409639, 0.8795180722891566, 0.7188755020080321], 'avgRecall': 0.7081317269076305, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T21:35:09.069291 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.66, 0.728, 0.5381526104417671, 0.8795180722891566, 0.7188755020080321], 'avgAccuracy': 0.7049092369477912, 'f1': [0.6668578089800687, 0.7409747899159663, 0.5389974962611953, 0.904961147962637, 0.725828271269728], 'avgF1': 0.7155239028779191, 'precision': [0.66, 0.728, 0.5381526104417671, 0.8795180722891566, 0.7188755020080321], 'avgPrecision': 0.7049092369477912, 'recall': [0.66, 0.728, 0.5381526104417671, 0.8795180722891566, 0.7188755020080321], 'avgRecall': 0.7049092369477912, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.792, 0.84, 0.8232931726907631, 0.927710843373494, 0.7269076305220884], 'avgAccuracy': 0.8219823293172691, 'f1': [0.7933593897012079, 0.8352161616161616, 0.8294962720494635, 0.9430378655752774, 0.7313628679313001], 'avgF1': 0.8264945113746821, 'precision': [0.792, 0.84, 0.8232931726907631, 0.927710843373494, 0.7269076305220884], 'avgPrecision': 0.8219823293172691, 'recall': [0.792, 0.84, 0.8232931726907631, 0.927710843373494, 0.7269076305220884], 'avgRecall': 0.8219823293172691, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.813960  0.818488   0.813960  0.813960   
1  0.758673  0.771995   0.758673  0.758673   
2  0.685658  0.691271   0.685658  0.685658   
3  0.629536  0.626839   0.629536  0.629536   
4  0.744993  0.756891   0.744993  0.744993   
5  0.757854  0.769570   0.757854  0.757854   
6  0.708132  0.718165   0.708132  0.708132   
7  0.688055  0.694262   0.688055  0.688055   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T21:42:30.199831 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.772, 0.84, 0.8353413654618473, 0.9236947791164659, 0.7188755020080321], 'avgAccuracy': 0.8179823293172691, 'f1': [0.772909546119789, 0.8343230654609732, 0.8416270059817923, 0.9388218244517653, 0.7231832682457618], 'avgF1': 0.8221729420520163, 'precision': [0.772, 0.84, 0.8353413654618473, 0.9236947791164659, 0.7188755020080321], 'avgPrecision': 0.8179823293172691, 'recall': [0.772, 0.84, 0.8353413654618473, 0.9236947791164659, 0.7188755020080321], 'avgRecall': 0.8179823293172691, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-28T21:42:37.473227 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.684, 0.76, 0.714859437751004, 0.8995983935742972, 0.7188755020080321], 'avgAccuracy': 0.7554666666666666, 'f1': [0.6922471857532823, 0.7706588121632503, 0.721473915398755, 0.9292748748538319, 0.7279228354461365], 'avgF1': 0.7683155247230512, 'precision': [0.684, 0.76, 0.714859437751004, 0.8995983935742972, 0.7188755020080321], 'avgPrecision': 0.7554666666666666, 'recall': [0.684, 0.76, 0.714859437751004, 0.8995983935742972, 0.7188755020080321], 'avgRecall': 0.7554666666666666, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T21:42:52.314882 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.636, 0.692, 0.5060240963855421, 0.8634538152610441, 0.6947791164658634], 'avgAccuracy': 0.67845140562249, 'f1': [0.6448814141324404, 0.7045493388644024, 0.48563548976377785, 0.8949370515770949, 0.7013312066920999], 'avgF1': 0.6862669002059631, 'precision': [0.636, 0.692, 0.5060240963855421, 0.8634538152610441, 0.6947791164658634], 'avgPrecision': 0.67845140562249, 'recall': [0.636, 0.692, 0.5060240963855421, 0.8634538152610441, 0.6947791164658634], 'avgRecall': 0.67845140562249, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T21:42:52.768010 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.584, 0.64, 0.44176706827309237, 0.8353413654618473, 0.6345381526104418], 'avgAccuracy': 0.6271293172690763, 'f1': [0.595006216194477, 0.636535593220339, 0.4586877367373483, 0.8348099571615193, 0.62606045425317], 'avgF1': 0.6302199915133707, 'precision': [0.584, 0.64, 0.44176706827309237, 0.8353413654618473, 0.6345381526104418], 'avgPrecision': 0.6271293172690763, 'recall': [0.584, 0.64, 0.44176706827309237, 0.8353413654618473, 0.6345381526104418], 'avgRecall': 0.6271293172690763, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T21:42:59.723726 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.7, 0.784, 0.714859437751004, 0.8594377510040161, 0.6666666666666666], 'avgAccuracy': 0.7449927710843374, 'f1': [0.6979960785782974, 0.7845305605341651, 0.7201478275748696, 0.8968389413892356, 0.6849408214870063], 'avgF1': 0.7568908459127148, 'precision': [0.7, 0.784, 0.714859437751004, 0.8594377510040161, 0.6666666666666666], 'avgPrecision': 0.7449927710843374, 'recall': [0.7, 0.784, 0.714859437751004, 0.8594377510040161, 0.6666666666666666], 'avgRecall': 0.7449927710843374, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T21:43:00.707905 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.716, 0.768, 0.8152610441767069, 0.9076305220883534, 0.6907630522088354], 'avgAccuracy': 0.7795309236947792, 'f1': [0.7192352975461911, 0.7709260808970713, 0.8214580596383672, 0.9314622227791003, 0.6994946292346419], 'avgF1': 0.7885152580190743, 'precision': [0.716, 0.768, 0.8152610441767069, 0.9076305220883534, 0.6907630522088354], 'avgPrecision': 0.7795309236947792, 'recall': [0.716, 0.768, 0.8152610441767069, 0.9076305220883534, 0.6907630522088354], 'avgRecall': 0.7795309236947792, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T21:43:03.311993 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.648, 0.7, 0.5100401606425703, 0.8594377510040161, 0.7028112449799196], 'avgAccuracy': 0.6840578313253012, 'f1': [0.6543442940038685, 0.7155075614045583, 0.4885014152406301, 0.8884051388020276, 0.7136764173922749], 'avgF1': 0.6920869653686719, 'precision': [0.648, 0.7, 0.5100401606425703, 0.8594377510040161, 0.7028112449799196], 'avgPrecision': 0.6840578313253012, 'recall': [0.648, 0.7, 0.5100401606425703, 0.8594377510040161, 0.7028112449799196], 'avgRecall': 0.6840578313253012, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T21:48:42.857274 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.66, 0.712, 0.570281124497992, 0.8634538152610441, 0.7108433734939759], 'avgAccuracy': 0.7033156626506024, 'f1': [0.666587234042553, 0.7263824175824176, 0.5752557267397114, 0.893184587174028, 0.7185597957070945], 'avgF1': 0.715993952249161, 'precision': [0.66, 0.712, 0.570281124497992, 0.8634538152610441, 0.7108433734939759], 'avgPrecision': 0.7033156626506024, 'recall': [0.66, 0.712, 0.570281124497992, 0.8634538152610441, 0.7108433734939759], 'avgRecall': 0.7033156626506024, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.772, 0.84, 0.8353413654618473, 0.9236947791164659, 0.7188755020080321], 'avgAccuracy': 0.8179823293172691, 'f1': [0.772909546119789, 0.8343230654609732, 0.8416270059817923, 0.9388218244517653, 0.7231832682457618], 'avgF1': 0.8221729420520163, 'precision': [0.772, 0.84, 0.8353413654618473, 0.9236947791164659, 0.7188755020080321], 'avgPrecision': 0.8179823293172691, 'recall': [0.772, 0.84, 0.8353413654618473, 0.9236947791164659, 0.7188755020080321], 'avgRecall': 0.8179823293172691, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.813982  0.818733   0.813982  0.813982   
1  0.753060  0.766252   0.753060  0.753060   
2  0.678451  0.686267   0.678451  0.678451   
3  0.627129  0.630220   0.627129  0.627129   
4  0.744993  0.756891   0.744993  0.744993   
5  0.765105  0.774485   0.765105  0.765105   
6  0.684058  0.692087   0.684058  0.684058   
7  0.684845  0.693596   0.684845  0.684845   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T21:56:01.674762 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.768, 0.84, 0.8232931726907631, 0.927710843373494, 0.7188755020080321], 'avgAccuracy': 0.8155759036144579, 'f1': [0.7699294056202101, 0.8390826371288213, 0.8295430119967707, 0.9410717381220082, 0.7248222669859513], 'avgF1': 0.8208898119707523, 'precision': [0.768, 0.84, 0.8232931726907631, 0.927710843373494, 0.7188755020080321], 'avgPrecision': 0.8155759036144579, 'recall': [0.768, 0.84, 0.8232931726907631, 0.927710843373494, 0.7188755020080321], 'avgRecall': 0.8155759036144579, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T21:56:08.829760 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.704, 0.756, 0.7028112449799196, 0.9076305220883534, 0.7108433734939759], 'avgAccuracy': 0.7562570281124498, 'f1': [0.7119396732299959, 0.7677376807706376, 0.7090957157455672, 0.9343201376936316, 0.7239720067072789], 'avgF1': 0.7694130428294222, 'precision': [0.704, 0.756, 0.7028112449799196, 0.9076305220883534, 0.7108433734939759], 'avgPrecision': 0.7562570281124498, 'recall': [0.704, 0.756, 0.7028112449799196, 0.9076305220883534, 0.7108433734939759], 'avgRecall': 0.7562570281124498, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T21:56:23.592757 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.632, 0.704, 0.46586345381526106, 0.8393574297188755, 0.6907630522088354], 'avgAccuracy': 0.6663967871485944, 'f1': [0.6408078085209663, 0.7163417395779722, 0.42833058513772376, 0.8756599431433599, 0.6977400910214915], 'avgF1': 0.6717760334803027, 'precision': [0.632, 0.704, 0.46586345381526106, 0.8393574297188755, 0.6907630522088354], 'avgPrecision': 0.6663967871485944, 'recall': [0.632, 0.704, 0.46586345381526106, 0.8393574297188755, 0.6907630522088354], 'avgRecall': 0.6663967871485944, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T21:56:23.983383 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.58, 0.628, 0.44176706827309237, 0.8353413654618473, 0.6345381526104418], 'avgAccuracy': 0.6239293172690763, 'f1': [0.5901852270249915, 0.6246739573679333, 0.4582884423127284, 0.8348099571615193, 0.6266545017397482], 'avgF1': 0.6269224171213842, 'precision': [0.58, 0.628, 0.44176706827309237, 0.8353413654618473, 0.6345381526104418], 'avgPrecision': 0.6239293172690763, 'recall': [0.58, 0.628, 0.44176706827309237, 0.8353413654618473, 0.6345381526104418], 'avgRecall': 0.6239293172690763, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T21:56:31.248339 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.704, 0.8, 0.7068273092369478, 0.8634538152610441, 0.6144578313253012], 'avgAccuracy': 0.7377477911646586, 'f1': [0.7006857163673454, 0.7993653977661273, 0.7130806018956604, 0.9012201023969025, 0.635247444831099], 'avgF1': 0.7499198526514269, 'precision': [0.704, 0.8, 0.7068273092369478, 0.8634538152610441, 0.6144578313253012], 'avgPrecision': 0.7377477911646586, 'recall': [0.704, 0.8, 0.7068273092369478, 0.8634538152610441, 0.6144578313253012], 'avgRecall': 0.7377477911646586, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T21:56:32.229189 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.728, 0.792, 0.8393574297188755, 0.9036144578313253, 0.6626506024096386], 'avgAccuracy': 0.7851244979919679, 'f1': [0.7335285136955545, 0.7944929115619529, 0.84566111603918, 0.9225093746832879, 0.6721379444905127], 'avgF1': 0.7936659720940976, 'precision': [0.728, 0.792, 0.8393574297188755, 0.9036144578313253, 0.6626506024096386], 'avgPrecision': 0.7851244979919679, 'recall': [0.728, 0.792, 0.8393574297188755, 0.9036144578313253, 0.6626506024096386], 'avgRecall': 0.7851244979919679, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T21:56:34.802145 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.648, 0.708, 0.5180722891566265, 0.8554216867469879, 0.6827309236947792], 'avgAccuracy': 0.6824449799196787, 'f1': [0.6543442940038685, 0.7231826227193875, 0.4963582865926183, 0.8852984973602275, 0.6948571924770515], 'avgF1': 0.6908081786306306, 'precision': [0.648, 0.708, 0.5180722891566265, 0.8554216867469879, 0.6827309236947792], 'avgPrecision': 0.6824449799196787, 'recall': [0.648, 0.708, 0.5180722891566265, 0.8554216867469879, 0.6827309236947792], 'avgRecall': 0.6824449799196787, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T22:01:07.007230 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.656, 0.716, 0.5341365461847389, 0.8273092369477911, 0.6907630522088354], 'avgAccuracy': 0.684841767068273, 'f1': [0.6627607582946096, 0.7310991638946327, 0.5354918644428278, 0.8636117503722756, 0.7020464999645473], 'avgF1': 0.6990020073937786, 'precision': [0.656, 0.716, 0.5341365461847389, 0.8273092369477911, 0.6907630522088354], 'avgPrecision': 0.684841767068273, 'recall': [0.656, 0.716, 0.5341365461847389, 0.8273092369477911, 0.6907630522088354], 'avgRecall': 0.684841767068273, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.768, 0.84, 0.8232931726907631, 0.927710843373494, 0.7188755020080321], 'avgAccuracy': 0.8155759036144579, 'f1': [0.7699294056202101, 0.8390826371288213, 0.8295430119967707, 0.9410717381220082, 0.7248222669859513], 'avgF1': 0.8208898119707523, 'precision': [0.768, 0.84, 0.8232931726907631, 0.927710843373494, 0.7188755020080321], 'avgPrecision': 0.8155759036144579, 'recall': [0.768, 0.84, 0.8232931726907631, 0.927710843373494, 0.7188755020080321], 'avgRecall': 0.8155759036144579, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.807557  0.813110   0.807557  0.807557   
1  0.753854  0.767800   0.753854  0.753854   
2  0.666397  0.671776   0.666397  0.666397   
3  0.623929  0.626922   0.623929  0.623929   
4  0.737748  0.749920   0.737748  0.737748   
5  0.765896  0.775972   0.765896  0.765896   
6  0.682445  0.690808   0.682445  0.682445   
7  0.680022  0.688909   0.680022  0.680022   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T22:08:49.380956 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.644, 0.692, 0.5943775100401606, 0.8514056224899599, 0.6506024096385542], 'avgAccuracy': 0.6864771084337349, 'f1': [0.647642959056718, 0.6858956591964466, 0.6409392710586637, 0.8527955722219878, 0.6488871321769386], 'avgF1': 0.695232118742151, 'precision': [0.644, 0.692, 0.5943775100401606, 0.8514056224899599, 0.6506024096385542], 'avgPrecision': 0.6864771084337349, 'recall': [0.644, 0.692, 0.5943775100401606, 0.8514056224899599, 0.6506024096385542], 'avgRecall': 0.6864771084337349, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T22:08:56.907575 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.592, 0.64, 0.5421686746987951, 0.8273092369477911, 0.6305220883534136], 'avgAccuracy': 0.6464, 'f1': [0.5998860061756097, 0.6464513480619676, 0.5676315444271304, 0.8347332098716466, 0.6297684613301456], 'avgF1': 0.6556941139733, 'precision': [0.592, 0.64, 0.5421686746987951, 0.8273092369477911, 0.6305220883534136], 'avgPrecision': 0.6464, 'recall': [0.592, 0.64, 0.5421686746987951, 0.8273092369477911, 0.6305220883534136], 'avgRecall': 0.6464, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T22:09:12.355796 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.604, 0.656, 0.43373493975903615, 0.7269076305220884, 0.6626506024096386], 'avgAccuracy': 0.6166586345381526, 'f1': [0.6159222172764769, 0.6576281917531595, 0.42607846300427843, 0.772008820808388, 0.6607953671133536], 'avgF1': 0.6264866119911313, 'precision': [0.604, 0.656, 0.43373493975903615, 0.7269076305220884, 0.6626506024096386], 'avgPrecision': 0.6166586345381526, 'recall': [0.604, 0.656, 0.43373493975903615, 0.7269076305220884, 0.6626506024096386], 'avgRecall': 0.6166586345381526, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T22:09:12.715188 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.58, 0.628, 0.44176706827309237, 0.8353413654618473, 0.6345381526104418], 'avgAccuracy': 0.6239293172690763, 'f1': [0.5879195229959204, 0.6224981684981685, 0.4594267231376977, 0.8348099571615193, 0.6266545017397482], 'avgF1': 0.6262617747066108, 'precision': [0.58, 0.628, 0.44176706827309237, 0.8353413654618473, 0.6345381526104418], 'avgPrecision': 0.6239293172690763, 'recall': [0.58, 0.628, 0.44176706827309237, 0.8353413654618473, 0.6345381526104418], 'avgRecall': 0.6239293172690763, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-28T22:09:19.951855 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.5, 0.628, 0.4738955823293173, 0.8032128514056225, 0.4859437751004016], 'avgAccuracy': 0.5782104417670683, 'f1': [0.5017889615842672, 0.6246355275325767, 0.5061752050904321, 0.8138106357531544, 0.4611029209026438], 'avgF1': 0.5815026501726148, 'precision': [0.5, 0.628, 0.4738955823293173, 0.8032128514056225, 0.4859437751004016], 'avgPrecision': 0.5782104417670683, 'recall': [0.5, 0.628, 0.4738955823293173, 0.8032128514056225, 0.4859437751004016], 'avgRecall': 0.5782104417670683, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 100, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T22:09:21.051115 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.608, 0.648, 0.5341365461847389, 0.8393574297188755, 0.6465863453815262], 'avgAccuracy': 0.6552160642570282, 'f1': [0.6146628276621946, 0.6386801843317972, 0.574532152692374, 0.8439362145254207, 0.6392654894971115], 'avgF1': 0.6622153737417796, 'precision': [0.608, 0.648, 0.5341365461847389, 0.8393574297188755, 0.6465863453815262], 'avgPrecision': 0.6552160642570282, 'recall': [0.608, 0.648, 0.5341365461847389, 0.8393574297188755, 0.6465863453815262], 'avgRecall': 0.6552160642570282, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-28T22:09:23.747461 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.604, 0.648, 0.4538152610441767, 0.7429718875502008, 0.6305220883534136], 'avgAccuracy': 0.6158618473895582, 'f1': [0.6169347220578373, 0.6484738302816723, 0.4663611343725105, 0.7642067030261591, 0.6258211569302842], 'avgF1': 0.6243595093336927, 'precision': [0.604, 0.648, 0.4538152610441767, 0.7429718875502008, 0.6305220883534136], 'avgPrecision': 0.6158618473895582, 'recall': [0.604, 0.648, 0.4538152610441767, 0.7429718875502008, 0.6305220883534136], 'avgRecall': 0.6158618473895582, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T22:11:03.033741 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.612, 0.66, 0.4538152610441767, 0.7469879518072289, 0.642570281124498], 'avgAccuracy': 0.6230746987951807, 'f1': [0.6246094486308962, 0.6680214727800935, 0.4559887411371683, 0.7779565875211168, 0.6357736044198344], 'avgF1': 0.6324699708978219, 'precision': [0.612, 0.66, 0.4538152610441767, 0.7469879518072289, 0.642570281124498], 'avgPrecision': 0.6230746987951807, 'recall': [0.612, 0.66, 0.4538152610441767, 0.7469879518072289, 0.642570281124498], 'avgRecall': 0.6230746987951807, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.644, 0.692, 0.5943775100401606, 0.8514056224899599, 0.6506024096385542], 'avgAccuracy': 0.6864771084337349, 'f1': [0.647642959056718, 0.6858956591964466, 0.6409392710586637, 0.8527955722219878, 0.6488871321769386], 'avgF1': 0.695232118742151, 'precision': [0.644, 0.692, 0.5943775100401606, 0.8514056224899599, 0.6506024096385542], 'avgPrecision': 0.6864771084337349, 'recall': [0.644, 0.692, 0.5943775100401606, 0.8514056224899599, 0.6506024096385542], 'avgRecall': 0.6864771084337349, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.683274  0.691576   0.683274  0.683274   
1  0.475322  0.462916   0.475322  0.475322   
2  0.616659  0.626487   0.616659  0.616659   
3  0.623929  0.626262   0.623929  0.623929   
4  0.568630  0.573068   0.568630  0.568630   
5  0.645584  0.655616   0.645584  0.645584   
6  0.615862  0.624360   0.615862  0.615862   
7  0.612652  0.619066   0.612652  0.612652   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T22:18:41.402240 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.612, 0.656, 0.5381526104417671, 0.8353413654618473, 0.6626506024096386], 'avgAccuracy': 0.6608289156626506, 'f1': [0.6121041355970933, 0.6657469445613413, 0.5744890589583157, 0.8397912326481907, 0.6554602587154591], 'avgF1': 0.66951832609608, 'precision': [0.612, 0.656, 0.5381526104417671, 0.8353413654618473, 0.6626506024096386], 'avgPrecision': 0.6608289156626506, 'recall': [0.612, 0.656, 0.5381526104417671, 0.8353413654618473, 0.6626506024096386], 'avgRecall': 0.6608289156626506, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-28T22:18:48.535845 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.616, 0.64, 0.4859437751004016, 0.8393574297188755, 0.6465863453815262], 'avgAccuracy': 0.6455775100401606, 'f1': [0.6277088827645514, 0.655394417560512, 0.5015120794416321, 0.8468982312227634, 0.6462145951757058], 'avgF1': 0.6555456412330329, 'precision': [0.616, 0.64, 0.4859437751004016, 0.8393574297188755, 0.6465863453815262], 'avgPrecision': 0.6455775100401606, 'recall': [0.616, 0.64, 0.4859437751004016, 0.8393574297188755, 0.6465863453815262], 'avgRecall': 0.6455775100401606, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 13, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T22:19:03.212306 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.608, 0.66, 0.42570281124497994, 0.7108433734939759, 0.6586345381526104], 'avgAccuracy': 0.6126361445783133, 'f1': [0.6183170636774872, 0.6614158567774936, 0.412666880906058, 0.7559525260665718, 0.6498973479178156], 'avgF1': 0.6196499350690853, 'precision': [0.608, 0.66, 0.42570281124497994, 0.7108433734939759, 0.6586345381526104], 'avgPrecision': 0.6126361445783133, 'recall': [0.608, 0.66, 0.42570281124497994, 0.7108433734939759, 0.6586345381526104], 'avgRecall': 0.6126361445783133, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T22:19:03.587308 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.58, 0.628, 0.43775100401606426, 0.8393574297188755, 0.6345381526104418], 'avgAccuracy': 0.6239293172690763, 'f1': [0.5865654969364207, 0.6224981684981685, 0.45591973995984364, 0.8385132438284361, 0.6266545017397482], 'avgF1': 0.6260302301925235, 'precision': [0.58, 0.628, 0.43775100401606426, 0.8393574297188755, 0.6345381526104418], 'avgPrecision': 0.6239293172690763, 'recall': [0.58, 0.628, 0.43775100401606426, 0.8393574297188755, 0.6345381526104418], 'avgRecall': 0.6239293172690763, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T22:19:10.993884 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.588, 0.52, 0.44176706827309237, 0.751004016064257, 0.6265060240963856], 'avgAccuracy': 0.585455421686747, 'f1': [0.5888060810724383, 0.5331240613994286, 0.467673529944063, 0.7710843373493976, 0.6279165595264623], 'avgF1': 0.5977209138583579, 'precision': [0.588, 0.52, 0.44176706827309237, 0.751004016064257, 0.6265060240963856], 'avgPrecision': 0.585455421686747, 'recall': [0.588, 0.52, 0.44176706827309237, 0.751004016064257, 0.6265060240963856], 'avgRecall': 0.585455421686747, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 100, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T22:19:12.030412 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.588, 0.62, 0.5220883534136547, 0.8393574297188755, 0.6626506024096386], 'avgAccuracy': 0.6464192771084337, 'f1': [0.588539706113235, 0.6316382643430067, 0.5591561648894429, 0.8443034473016638, 0.6694314948235153], 'avgF1': 0.6586138154941727, 'precision': [0.588, 0.62, 0.5220883534136547, 0.8393574297188755, 0.6626506024096386], 'avgPrecision': 0.6464192771084337, 'recall': [0.588, 0.62, 0.5220883534136547, 0.8393574297188755, 0.6626506024096386], 'avgRecall': 0.6464192771084337, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T22:19:14.530938 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.604, 0.648, 0.4578313253012048, 0.7389558232931727, 0.6465863453815262], 'avgAccuracy': 0.6190746987951807, 'f1': [0.6162588945548326, 0.6498739272817095, 0.4721268142923631, 0.7634234416658229, 0.6411749156200647], 'avgF1': 0.6285715986829585, 'precision': [0.604, 0.648, 0.4578313253012048, 0.7389558232931727, 0.6465863453815262], 'avgPrecision': 0.6190746987951807, 'recall': [0.604, 0.648, 0.4578313253012048, 0.7389558232931727, 0.6465863453815262], 'avgRecall': 0.6190746987951807, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T22:20:21.058950 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.608, 0.66, 0.44176706827309237, 0.7349397590361446, 0.642570281124498], 'avgAccuracy': 0.6174554216867469, 'f1': [0.6195255377533858, 0.6614158567774936, 0.4446112100427354, 0.7635685474519565, 0.6380008565448918], 'avgF1': 0.6254244017140926, 'precision': [0.608, 0.66, 0.44176706827309237, 0.7349397590361446, 0.642570281124498], 'avgPrecision': 0.6174554216867469, 'recall': [0.608, 0.66, 0.44176706827309237, 0.7349397590361446, 0.642570281124498], 'avgRecall': 0.6174554216867469, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.612, 0.656, 0.5381526104417671, 0.8353413654618473, 0.6626506024096386], 'avgAccuracy': 0.6608289156626506, 'f1': [0.6121041355970933, 0.6657469445613413, 0.5744890589583157, 0.8397912326481907, 0.6554602587154591], 'avgF1': 0.66951832609608, 'precision': [0.612, 0.656, 0.5381526104417671, 0.8353413654618473, 0.6626506024096386], 'avgPrecision': 0.6608289156626506, 'recall': [0.612, 0.656, 0.5381526104417671, 0.8353413654618473, 0.6626506024096386], 'avgRecall': 0.6608289156626506, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.652006  0.660246   0.652006  0.652006   
1  0.466509  0.454747   0.466509  0.466509   
2  0.612636  0.619650   0.612636  0.612636   
3  0.623929  0.626030   0.623929  0.623929   
4  0.568675  0.577539   0.568675  0.568675   
5  0.627142  0.637205   0.627142  0.627142   
6  0.619075  0.628572   0.619075  0.619075   
7  0.612643  0.624516   0.612643  0.612643   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T22:27:33.599202 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.612, 0.644, 0.5542168674698795, 0.8313253012048193, 0.6706827309236948], 'avgAccuracy': 0.6624449799196788, 'f1': [0.6121041355970933, 0.6513908123061447, 0.597356097285909, 0.8370287874185819, 0.6695824103322116], 'avgF1': 0.6734924485879881, 'precision': [0.612, 0.644, 0.5542168674698795, 0.8313253012048193, 0.6706827309236948], 'avgPrecision': 0.6624449799196788, 'recall': [0.612, 0.644, 0.5542168674698795, 0.8313253012048193, 0.6706827309236948], 'avgRecall': 0.6624449799196788, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T22:27:40.693540 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.6, 0.636, 0.5100401606425703, 0.8473895582329317, 0.642570281124498], 'avgAccuracy': 0.6472, 'f1': [0.606024215023414, 0.6490201990819379, 0.5280082659320139, 0.8538525995875392, 0.6385067687778532], 'avgF1': 0.6550824096805516, 'precision': [0.6, 0.636, 0.5100401606425703, 0.8473895582329317, 0.642570281124498], 'avgPrecision': 0.6472, 'recall': [0.6, 0.636, 0.5100401606425703, 0.8473895582329317, 0.642570281124498], 'avgRecall': 0.6472, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 13, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T22:27:56.140759 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.616, 0.66, 0.42168674698795183, 0.6987951807228916, 0.6706827309236948], 'avgAccuracy': 0.6134329317269076, 'f1': [0.6267198965210661, 0.6614158567774936, 0.4034062270972491, 0.7419761048068823, 0.6725846258990003], 'avgF1': 0.6212205422203383, 'precision': [0.616, 0.66, 0.42168674698795183, 0.6987951807228916, 0.6706827309236948], 'avgPrecision': 0.6134329317269076, 'recall': [0.616, 0.66, 0.42168674698795183, 0.6987951807228916, 0.6706827309236948], 'avgRecall': 0.6134329317269076, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T22:27:56.547011 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.584, 0.64, 0.44176706827309237, 0.8393574297188755, 0.6465863453815262], 'avgAccuracy': 0.6303421686746988, 'f1': [0.5907148328135997, 0.6331520065987755, 0.45881905182646254, 0.8385132438284361, 0.639136360682928], 'avgF1': 0.6320670991500403, 'precision': [0.584, 0.64, 0.44176706827309237, 0.8393574297188755, 0.6465863453815262], 'avgPrecision': 0.6303421686746988, 'recall': [0.584, 0.64, 0.44176706827309237, 0.8393574297188755, 0.6465863453815262], 'avgRecall': 0.6303421686746988, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T22:28:04.258806 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.568, 0.644, 0.4457831325301205, 0.7991967871485943, 0.6104417670682731], 'avgAccuracy': 0.6134843373493976, 'f1': [0.569455373148017, 0.6516575431469049, 0.4740473867071771, 0.8086431203320488, 0.6046307842439502], 'avgF1': 0.6216868415156196, 'precision': [0.568, 0.644, 0.4457831325301205, 0.7991967871485943, 0.6104417670682731], 'avgPrecision': 0.6134843373493976, 'recall': [0.568, 0.644, 0.4457831325301205, 0.7991967871485943, 0.6104417670682731], 'avgRecall': 0.6134843373493976, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T22:28:05.315941 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.584, 0.64, 0.5220883534136547, 0.8313253012048193, 0.678714859437751], 'avgAccuracy': 0.651225702811245, 'f1': [0.5883440805696325, 0.64964212294124, 0.5581024954518932, 0.8379079084248814, 0.6762029753645132], 'avgF1': 0.662039916550432, 'precision': [0.584, 0.64, 0.5220883534136547, 0.8313253012048193, 0.678714859437751], 'avgPrecision': 0.651225702811245, 'recall': [0.584, 0.64, 0.5220883534136547, 0.8313253012048193, 0.678714859437751], 'avgRecall': 0.651225702811245, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T22:28:07.868463 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.592, 0.648, 0.46184738955823296, 0.7389558232931727, 0.6626506024096386], 'avgAccuracy': 0.6206907630522088, 'f1': [0.6017632341072021, 0.6498739272817095, 0.4796837665862335, 0.7634234416658229, 0.6531117627891821], 'avgF1': 0.62957122648603, 'precision': [0.592, 0.648, 0.46184738955823296, 0.7389558232931727, 0.6626506024096386], 'avgPrecision': 0.6206907630522088, 'recall': [0.592, 0.648, 0.46184738955823296, 0.7389558232931727, 0.6626506024096386], 'avgRecall': 0.6206907630522088, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T22:29:12.442475 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.62, 0.644, 0.44176706827309237, 0.7228915662650602, 0.6706827309236948], 'avgAccuracy': 0.6198682730923695, 'f1': [0.6309104503504619, 0.6462082088655747, 0.4446112100427354, 0.7521219513509532, 0.6608479570780306], 'avgF1': 0.6269399555375511, 'precision': [0.62, 0.644, 0.44176706827309237, 0.7228915662650602, 0.6706827309236948], 'avgPrecision': 0.6198682730923695, 'recall': [0.62, 0.644, 0.44176706827309237, 0.7228915662650602, 0.6706827309236948], 'avgRecall': 0.6198682730923695, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.612, 0.644, 0.5542168674698795, 0.8313253012048193, 0.6706827309236948], 'avgAccuracy': 0.6624449799196788, 'f1': [0.6121041355970933, 0.6513908123061447, 0.597356097285909, 0.8370287874185819, 0.6695824103322116], 'avgF1': 0.6734924485879881, 'precision': [0.612, 0.644, 0.5542168674698795, 0.8313253012048193, 0.6706827309236948], 'avgPrecision': 0.6624449799196788, 'recall': [0.612, 0.644, 0.5542168674698795, 0.8313253012048193, 0.6706827309236948], 'avgRecall': 0.6624449799196788, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.662445  0.673492   0.662445  0.662445   
1  0.474519  0.464603   0.474519  0.474519   
2  0.612636  0.619530   0.612636  0.612636   
3  0.630342  0.632067   0.630342  0.630342   
4  0.613484  0.621687   0.613484  0.613484   
5  0.643197  0.654199   0.643197  0.643197   
6  0.620691  0.629571   0.620691  0.620691   
7  0.619868  0.626940   0.619868  0.619868   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T22:36:25.289032 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.608, 0.66, 0.5341365461847389, 0.8273092369477911, 0.6907630522088354], 'avgAccuracy': 0.6640417670682731, 'f1': [0.612803420349091, 0.6682190220710633, 0.5729305190489704, 0.8342254170744602, 0.6923216720154846], 'avgF1': 0.6761000101118139, 'precision': [0.608, 0.66, 0.5341365461847389, 0.8273092369477911, 0.6907630522088354], 'avgPrecision': 0.6640417670682731, 'recall': [0.608, 0.66, 0.5341365461847389, 0.8273092369477911, 0.6907630522088354], 'avgRecall': 0.6640417670682731, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T22:36:32.413695 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.596, 0.644, 0.4899598393574297, 0.8273092369477911, 0.6546184738955824], 'avgAccuracy': 0.6423775100401606, 'f1': [0.6037203302695122, 0.656534342915983, 0.5076579488158941, 0.8380630080475228, 0.6519312457255116], 'avgF1': 0.6515813751548848, 'precision': [0.596, 0.644, 0.4899598393574297, 0.8273092369477911, 0.6546184738955824], 'avgPrecision': 0.6423775100401606, 'recall': [0.596, 0.644, 0.4899598393574297, 0.8273092369477911, 0.6546184738955824], 'avgRecall': 0.6423775100401606, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T22:36:47.268200 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.608, 0.66, 0.42168674698795183, 0.7108433734939759, 0.6706827309236948], 'avgAccuracy': 0.6142425702811245, 'f1': [0.6183170636774872, 0.6614158567774936, 0.4034062270972491, 0.7585047883843066, 0.6604380726399312], 'avgF1': 0.6204164017152936, 'precision': [0.608, 0.66, 0.42168674698795183, 0.7108433734939759, 0.6706827309236948], 'avgPrecision': 0.6142425702811245, 'recall': [0.608, 0.66, 0.42168674698795183, 0.7108433734939759, 0.6706827309236948], 'avgRecall': 0.6142425702811245, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T22:36:47.627587 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.58, 0.632, 0.42168674698795183, 0.8353413654618473, 0.6546184738955824], 'avgAccuracy': 0.6247293172690763, 'f1': [0.5865654969364207, 0.6240476761524063, 0.43312578479291847, 0.8365466017757426, 0.6479132035539954], 'avgF1': 0.6256397526422967, 'precision': [0.58, 0.632, 0.42168674698795183, 0.8353413654618473, 0.6546184738955824], 'avgPrecision': 0.6247293172690763, 'recall': [0.58, 0.632, 0.42168674698795183, 0.8353413654618473, 0.6546184738955824], 'avgRecall': 0.6247293172690763, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-28T22:36:55.133420 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.592, 0.6, 0.44176706827309237, 0.7108433734939759, 0.6987951807228916], 'avgAccuracy': 0.6086811244979919, 'f1': [0.5829662653936627, 0.610520441703825, 0.4651248672852329, 0.7339334871968092, 0.6786939032117055], 'avgF1': 0.614247792958247, 'precision': [0.592, 0.6, 0.44176706827309237, 0.7108433734939759, 0.6987951807228916], 'avgPrecision': 0.6086811244979919, 'recall': [0.592, 0.6, 0.44176706827309237, 0.7108433734939759, 0.6987951807228916], 'avgRecall': 0.6086811244979919, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-28T22:36:56.089576 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.608, 0.624, 0.5341365461847389, 0.8313253012048193, 0.6626506024096386], 'avgAccuracy': 0.6520224899598394, 'f1': [0.6116112865635344, 0.6359847031749104, 0.5785479240327648, 0.8388185922805564, 0.6672506639821383], 'avgF1': 0.6664426340067808, 'precision': [0.608, 0.624, 0.5341365461847389, 0.8313253012048193, 0.6626506024096386], 'avgPrecision': 0.6520224899598394, 'recall': [0.608, 0.624, 0.5341365461847389, 0.8313253012048193, 0.6626506024096386], 'avgRecall': 0.6520224899598394, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-28T22:36:58.527207 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.592, 0.648, 0.46184738955823296, 0.7389558232931727, 0.6626506024096386], 'avgAccuracy': 0.6206907630522088, 'f1': [0.6017632341072021, 0.6498739272817095, 0.4796837665862335, 0.7634234416658229, 0.6531117627891821], 'avgF1': 0.62957122648603, 'precision': [0.592, 0.648, 0.46184738955823296, 0.7389558232931727, 0.6626506024096386], 'avgPrecision': 0.6206907630522088, 'recall': [0.592, 0.648, 0.46184738955823296, 0.7389558232931727, 0.6626506024096386], 'avgRecall': 0.6206907630522088, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T22:38:02.323461 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.608, 0.66, 0.43775100401606426, 0.7349397590361446, 0.6666666666666666], 'avgAccuracy': 0.6214714859437751, 'f1': [0.6189703710940503, 0.6614158567774936, 0.4386888821345646, 0.7655028647232759, 0.6567693885141245], 'avgF1': 0.6282694726487018, 'precision': [0.608, 0.66, 0.43775100401606426, 0.7349397590361446, 0.6666666666666666], 'avgPrecision': 0.6214714859437751, 'recall': [0.608, 0.66, 0.43775100401606426, 0.7349397590361446, 0.6666666666666666], 'avgRecall': 0.6214714859437751, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.608, 0.66, 0.5341365461847389, 0.8273092369477911, 0.6907630522088354], 'avgAccuracy': 0.6640417670682731, 'f1': [0.612803420349091, 0.6682190220710633, 0.5729305190489704, 0.8342254170744602, 0.6923216720154846], 'avgF1': 0.6761000101118139, 'precision': [0.608, 0.66, 0.5341365461847389, 0.8273092369477911, 0.6907630522088354], 'avgPrecision': 0.6640417670682731, 'recall': [0.608, 0.66, 0.5341365461847389, 0.8273092369477911, 0.6907630522088354], 'avgRecall': 0.6640417670682731, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.660035  0.669980   0.660035  0.660035   
1  0.470503  0.461693   0.470503  0.470503   
2  0.614243  0.620416   0.614243  0.614243   
3  0.624729  0.625640   0.624729  0.624729   
4  0.593497  0.605620   0.593497  0.593497   
5  0.635955  0.648851   0.635955  0.635955   
6  0.620691  0.629571   0.620691  0.620691   
7  0.611839  0.615364   0.611839  0.611839   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T22:45:14.866473 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.62, 0.664, 0.5220883534136547, 0.8313253012048193, 0.6867469879518072], 'avgAccuracy': 0.6648321285140563, 'f1': [0.6208806286497008, 0.6669812244897959, 0.5593282219788243, 0.8370287874185819, 0.689515805341587], 'avgF1': 0.6747469335756979, 'precision': [0.62, 0.664, 0.5220883534136547, 0.8313253012048193, 0.6867469879518072], 'avgPrecision': 0.6648321285140563, 'recall': [0.62, 0.664, 0.5220883534136547, 0.8313253012048193, 0.6867469879518072], 'avgRecall': 0.6648321285140563, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-28T22:45:22.152560 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.604, 0.652, 0.4738955823293173, 0.7871485943775101, 0.6626506024096386], 'avgAccuracy': 0.6359389558232932, 'f1': [0.6086027452310089, 0.6578927272727274, 0.4859287496058938, 0.8289544451721248, 0.6551489187902864], 'avgF1': 0.6473055172144082, 'precision': [0.604, 0.652, 0.4738955823293173, 0.7871485943775101, 0.6626506024096386], 'avgPrecision': 0.6359389558232932, 'recall': [0.604, 0.652, 0.4738955823293173, 0.7871485943775101, 0.6626506024096386], 'avgRecall': 0.6359389558232932, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
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Processing Model: LogisticRegression
2021-05-28T22:45:37.959112 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.604, 0.636, 0.40562248995983935, 0.7188755020080321, 0.6626506024096386], 'avgAccuracy': 0.605429718875502, 'f1': [0.6116246118197338, 0.6385392658950373, 0.3899578016779831, 0.7671227898017409, 0.6574356169672013], 'avgF1': 0.6129360172323393, 'precision': [0.604, 0.636, 0.40562248995983935, 0.7188755020080321, 0.6626506024096386], 'avgPrecision': 0.605429718875502, 'recall': [0.604, 0.636, 0.40562248995983935, 0.7188755020080321, 0.6626506024096386], 'avgRecall': 0.605429718875502, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T22:45:38.365371 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.58, 0.62, 0.41365461847389556, 0.8313253012048193, 0.6506024096385542], 'avgAccuracy': 0.6191164658634538, 'f1': [0.5865654969364207, 0.6126864253393665, 0.42284783291078454, 0.8327734417754876, 0.6452418547212819], 'avgF1': 0.6200230103366683, 'precision': [0.58, 0.62, 0.41365461847389556, 0.8313253012048193, 0.6506024096385542], 'avgPrecision': 0.6191164658634538, 'recall': [0.58, 0.62, 0.41365461847389556, 0.8313253012048193, 0.6506024096385542], 'avgRecall': 0.6191164658634538, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-28T22:45:45.870967 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.588, 0.66, 0.4538152610441767, 0.7309236947791165, 0.6907630522088354], 'avgAccuracy': 0.6247004016064257, 'f1': [0.5803308431223256, 0.6632430295152123, 0.48230091871676295, 0.752860180216665, 0.6703386244449581], 'avgF1': 0.6298147192031848, 'precision': [0.588, 0.66, 0.4538152610441767, 0.7309236947791165, 0.6907630522088354], 'avgPrecision': 0.6247004016064257, 'recall': [0.588, 0.66, 0.4538152610441767, 0.7309236947791165, 0.6907630522088354], 'avgRecall': 0.6247004016064257, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-28T22:45:46.794429 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.612, 0.64, 0.5140562248995983, 0.8393574297188755, 0.6666666666666666], 'avgAccuracy': 0.6544160642570281, 'f1': [0.6149688513037349, 0.6465245761093054, 0.5519843436329912, 0.8443034473016638, 0.6701331381049564], 'avgF1': 0.6655828712905304, 'precision': [0.612, 0.64, 0.5140562248995983, 0.8393574297188755, 0.6666666666666666], 'avgPrecision': 0.6544160642570281, 'recall': [0.612, 0.64, 0.5140562248995983, 0.8393574297188755, 0.6666666666666666], 'avgRecall': 0.6544160642570281, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T22:45:49.221506 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.608, 0.628, 0.4457831325301205, 0.7349397590361446, 0.6546184738955824], 'avgAccuracy': 0.6142682730923695, 'f1': [0.6176947697111632, 0.6305924557119835, 0.454735101778283, 0.7585636664304276, 0.6394214428872986], 'avgF1': 0.6202014873038312, 'precision': [0.608, 0.628, 0.4457831325301205, 0.7349397590361446, 0.6546184738955824], 'avgPrecision': 0.6142682730923695, 'recall': [0.608, 0.628, 0.4457831325301205, 0.7349397590361446, 0.6546184738955824], 'avgRecall': 0.6142682730923695, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T22:46:33.658891 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.608, 0.644, 0.40562248995983935, 0.714859437751004, 0.6666666666666666], 'avgAccuracy': 0.607829718875502, 'f1': [0.6146542721000553, 0.6433845524296675, 0.3899578016779831, 0.764287920914427, 0.6630002643219388], 'avgF1': 0.6150569622888143, 'precision': [0.608, 0.644, 0.40562248995983935, 0.714859437751004, 0.6666666666666666], 'avgPrecision': 0.607829718875502, 'recall': [0.608, 0.644, 0.40562248995983935, 0.714859437751004, 0.6666666666666666], 'avgRecall': 0.607829718875502, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.62, 0.664, 0.5220883534136547, 0.8313253012048193, 0.6867469879518072], 'avgAccuracy': 0.6648321285140563, 'f1': [0.6208806286497008, 0.6669812244897959, 0.5593282219788243, 0.8370287874185819, 0.689515805341587], 'avgF1': 0.6747469335756979, 'precision': [0.62, 0.664, 0.5220883534136547, 0.8313253012048193, 0.6867469879518072], 'avgPrecision': 0.6648321285140563, 'recall': [0.62, 0.664, 0.5220883534136547, 0.8313253012048193, 0.6867469879518072], 'avgRecall': 0.6648321285140563, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.660826  0.670647   0.660826  0.660826   
1  0.476903  0.463214   0.476903  0.476903   
2  0.605430  0.612936   0.605430  0.605430   
3  0.619116  0.620023   0.619116  0.619116   
4  0.606316  0.617295   0.606316  0.606316   
5  0.638345  0.650812   0.638345  0.638345   
6  0.614268  0.620201   0.614268  0.614268   
7  0.603014  0.615198   0.603014  0.603014   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T22:53:34.089124 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.596, 0.656, 0.4819277108433735, 0.8152610441767069, 0.6666666666666666], 'avgAccuracy': 0.6431710843373494, 'f1': [0.6032931326372981, 0.6670598013750955, 0.4974433312740977, 0.8255604541926298, 0.6722169912800455], 'avgF1': 0.6531147421518333, 'precision': [0.596, 0.656, 0.4819277108433735, 0.8152610441767069, 0.6666666666666666], 'avgPrecision': 0.6431710843373494, 'recall': [0.596, 0.656, 0.4819277108433735, 0.8152610441767069, 0.6666666666666666], 'avgRecall': 0.6431710843373494, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T22:53:41.083874 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.592, 0.62, 0.4538152610441767, 0.7710843373493976, 0.6666666666666666], 'avgAccuracy': 0.6207132530120482, 'f1': [0.6011913733340857, 0.6341662380300956, 0.4482238368143228, 0.8115882332749802, 0.6629678295711333], 'avgF1': 0.6316275022049236, 'precision': [0.592, 0.62, 0.4538152610441767, 0.7710843373493976, 0.6666666666666666], 'avgPrecision': 0.6207132530120482, 'recall': [0.592, 0.62, 0.4538152610441767, 0.7710843373493976, 0.6666666666666666], 'avgRecall': 0.6207132530120482, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 15, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T22:53:55.532021 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.604, 0.636, 0.40963855421686746, 0.7068273092369478, 0.6546184738955824], 'avgAccuracy': 0.6022168674698796, 'f1': [0.6141031857031857, 0.6385392658950373, 0.38408029215270306, 0.7540652118965372, 0.6579839276752074], 'avgF1': 0.6097543766645341, 'precision': [0.604, 0.636, 0.40963855421686746, 0.7068273092369478, 0.6546184738955824], 'avgPrecision': 0.6022168674698796, 'recall': [0.604, 0.636, 0.40963855421686746, 0.7068273092369478, 0.6546184738955824], 'avgRecall': 0.6022168674698796, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T22:53:55.891398 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.576, 0.624, 0.42971887550200805, 0.8192771084337349, 0.6586345381526104], 'avgAccuracy': 0.6215261044176706, 'f1': [0.5816290435452378, 0.618200856286942, 0.4377133893970274, 0.8210143703183825, 0.65101656501217], 'avgF1': 0.6219148449119519, 'precision': [0.576, 0.624, 0.42971887550200805, 0.8192771084337349, 0.6586345381526104], 'avgPrecision': 0.6215261044176706, 'recall': [0.576, 0.624, 0.42971887550200805, 0.8192771084337349, 0.6586345381526104], 'avgRecall': 0.6215261044176706, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T22:54:03.175666 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.596, 0.46, 0.46987951807228917, 0.7630522088353414, 0.5341365461847389], 'avgAccuracy': 0.5646136546184739, 'f1': [0.5973077612701495, 0.46155421776161565, 0.503314748008694, 0.8005179023303244, 0.5311410963742251], 'avgF1': 0.5787671451490017, 'precision': [0.596, 0.46, 0.46987951807228917, 0.7630522088353414, 0.5341365461847389], 'avgPrecision': 0.5646136546184739, 'recall': [0.596, 0.46, 0.46987951807228917, 0.7630522088353414, 0.5341365461847389], 'avgRecall': 0.5646136546184739, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T22:54:04.364344 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.592, 0.62, 0.4859437751004016, 0.8152610441767069, 0.6506024096385542], 'avgAccuracy': 0.6327614457831325, 'f1': [0.6011913733340857, 0.6341662380300956, 0.5068982434407437, 0.8273502590546044, 0.6601757030034601], 'avgF1': 0.6459563633725979, 'precision': [0.592, 0.62, 0.4859437751004016, 0.8152610441767069, 0.6506024096385542], 'avgPrecision': 0.6327614457831325, 'recall': [0.592, 0.62, 0.4859437751004016, 0.8152610441767069, 0.6506024096385542], 'avgRecall': 0.6327614457831325, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T22:54:06.800758 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.6, 0.628, 0.4457831325301205, 0.7349397590361446, 0.6626506024096386], 'avgAccuracy': 0.6142746987951807, 'f1': [0.6114655367231637, 0.6305924557119835, 0.454735101778283, 0.7585636664304276, 0.6463966478770088], 'avgF1': 0.6203506817041733, 'precision': [0.6, 0.628, 0.4457831325301205, 0.7349397590361446, 0.6626506024096386], 'avgPrecision': 0.6142746987951807, 'recall': [0.6, 0.628, 0.4457831325301205, 0.7349397590361446, 0.6626506024096386], 'avgRecall': 0.6142746987951807, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T22:54:51.866120 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.604, 0.636, 0.41365461847389556, 0.714859437751004, 0.6626506024096386], 'avgAccuracy': 0.6062329317269076, 'f1': [0.6141031857031857, 0.6385392658950373, 0.3905689047995097, 0.764287920914427, 0.655367336684385], 'avgF1': 0.6125733227993089, 'precision': [0.604, 0.636, 0.41365461847389556, 0.714859437751004, 0.6626506024096386], 'avgPrecision': 0.6062329317269076, 'recall': [0.604, 0.636, 0.41365461847389556, 0.714859437751004, 0.6626506024096386], 'avgRecall': 0.6062329317269076, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.596, 0.656, 0.4819277108433735, 0.8152610441767069, 0.6666666666666666], 'avgAccuracy': 0.6431710843373494, 'f1': [0.6032931326372981, 0.6670598013750955, 0.4974433312740977, 0.8255604541926298, 0.6722169912800455], 'avgF1': 0.6531147421518333, 'precision': [0.596, 0.656, 0.4819277108433735, 0.8152610441767069, 0.6666666666666666], 'avgPrecision': 0.6431710843373494, 'recall': [0.596, 0.656, 0.4819277108433735, 0.8152610441767069, 0.6666666666666666], 'avgRecall': 0.6431710843373494, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.639968  0.650503   0.639968  0.639968   
1  0.472919  0.459770   0.472919  0.472919   
2  0.602217  0.607305   0.602217  0.602217   
3  0.621526  0.621915   0.621526  0.621526   
4  0.564614  0.578767   0.564614  0.564614   
5  0.626342  0.636403   0.626342  0.626342   
6  0.614275  0.620351   0.614275  0.614275   
7  0.603017  0.608671   0.603017  0.603017   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T23:02:05.425973 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6, 0.648, 0.4979919678714859, 0.8273092369477911, 0.6506024096385542], 'avgAccuracy': 0.6447807228915663, 'f1': [0.6084049734108214, 0.6596198087264065, 0.5293950019320559, 0.8298189700448412, 0.6564858750258641], 'avgF1': 0.6567449258279978, 'precision': [0.6, 0.648, 0.4979919678714859, 0.8273092369477911, 0.6506024096385542], 'avgPrecision': 0.6447807228915663, 'recall': [0.6, 0.648, 0.4979919678714859, 0.8273092369477911, 0.6506024096385542], 'avgRecall': 0.6447807228915663, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T23:02:13.795147 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6, 0.628, 0.42971887550200805, 0.7389558232931727, 0.6546184738955824], 'avgAccuracy': 0.6102586345381527, 'f1': [0.6102253571117878, 0.6418048910702305, 0.4077950992565974, 0.7831244077433329, 0.6498505217782325], 'avgF1': 0.6185600553920363, 'precision': [0.6, 0.628, 0.42971887550200805, 0.7389558232931727, 0.6546184738955824], 'avgPrecision': 0.6102586345381527, 'recall': [0.6, 0.628, 0.42971887550200805, 0.7389558232931727, 0.6546184738955824], 'avgRecall': 0.6102586345381527, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 15, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T23:02:30.447126 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.604, 0.632, 0.41365461847389556, 0.7068273092369478, 0.642570281124498], 'avgAccuracy': 0.5998104417670682, 'f1': [0.6141031857031857, 0.6346857273868014, 0.3905689047995097, 0.7540652118965372, 0.6497839147258705], 'avgF1': 0.6086413889023808, 'precision': [0.604, 0.632, 0.41365461847389556, 0.7068273092369478, 0.642570281124498], 'avgPrecision': 0.5998104417670682, 'recall': [0.604, 0.632, 0.41365461847389556, 0.7068273092369478, 0.642570281124498], 'avgRecall': 0.5998104417670682, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T23:02:30.806500 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.588, 0.636, 0.41767068273092367, 0.7831325301204819, 0.6506024096385542], 'avgAccuracy': 0.6150811244979919, 'f1': [0.5948966346627165, 0.6327143850643705, 0.40514105112963505, 0.7809332568368713, 0.6458481914776659], 'avgF1': 0.6119067038342518, 'precision': [0.588, 0.636, 0.41767068273092367, 0.7831325301204819, 0.6506024096385542], 'avgPrecision': 0.6150811244979919, 'recall': [0.588, 0.636, 0.41767068273092367, 0.7831325301204819, 0.6506024096385542], 'avgRecall': 0.6150811244979919, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T23:02:38.180937 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.604, 0.5, 0.4497991967871486, 0.7309236947791165, 0.4979919678714859], 'avgAccuracy': 0.5565429718875502, 'f1': [0.5975477855477854, 0.5104181724315953, 0.4782792415552194, 0.7532984935316382, 0.47917401552455574], 'avgF1': 0.5637435417181588, 'precision': [0.604, 0.5, 0.4497991967871486, 0.7309236947791165, 0.4979919678714859], 'avgPrecision': 0.5565429718875502, 'recall': [0.604, 0.5, 0.4497991967871486, 0.7309236947791165, 0.4979919678714859], 'avgRecall': 0.5565429718875502, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T23:02:39.201783 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6, 0.628, 0.4939759036144578, 0.8313253012048193, 0.6506024096385542], 'avgAccuracy': 0.6407807228915663, 'f1': [0.6102253571117878, 0.6418048910702305, 0.5252768402191668, 0.8334790908678135, 0.6564858750258641], 'avgF1': 0.6534544108589725, 'precision': [0.6, 0.628, 0.4939759036144578, 0.8313253012048193, 0.6506024096385542], 'avgPrecision': 0.6407807228915663, 'recall': [0.6, 0.628, 0.4939759036144578, 0.8313253012048193, 0.6506024096385542], 'avgRecall': 0.6407807228915663, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-28T23:02:41.722609 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6, 0.628, 0.4457831325301205, 0.7349397590361446, 0.6626506024096386], 'avgAccuracy': 0.6142746987951807, 'f1': [0.6114655367231637, 0.6305924557119835, 0.454735101778283, 0.7585636664304276, 0.6463966478770088], 'avgF1': 0.6203506817041733, 'precision': [0.6, 0.628, 0.4457831325301205, 0.7349397590361446, 0.6626506024096386], 'avgPrecision': 0.6142746987951807, 'recall': [0.6, 0.628, 0.4457831325301205, 0.7349397590361446, 0.6626506024096386], 'avgRecall': 0.6142746987951807, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T23:03:28.030534 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.604, 0.632, 0.41365461847389556, 0.714859437751004, 0.6465863453815262], 'avgAccuracy': 0.6022200803212852, 'f1': [0.6141031857031857, 0.6346857273868014, 0.3996059752104363, 0.7628564524663775, 0.6434714414812377], 'avgF1': 0.6109445564496077, 'precision': [0.604, 0.632, 0.41365461847389556, 0.714859437751004, 0.6465863453815262], 'avgPrecision': 0.6022200803212852, 'recall': [0.604, 0.632, 0.41365461847389556, 0.714859437751004, 0.6465863453815262], 'avgRecall': 0.6022200803212852, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6, 0.628, 0.4939759036144578, 0.8313253012048193, 0.6506024096385542], 'avgAccuracy': 0.6407807228915663, 'f1': [0.6102253571117878, 0.6418048910702305, 0.5252768402191668, 0.8334790908678135, 0.6564858750258641], 'avgF1': 0.6534544108589725, 'precision': [0.6, 0.628, 0.4939759036144578, 0.8313253012048193, 0.6506024096385542], 'avgPrecision': 0.6407807228915663, 'recall': [0.6, 0.628, 0.4939759036144578, 0.8313253012048193, 0.6506024096385542], 'avgRecall': 0.6407807228915663, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.624704  0.630677   0.624704  0.624704   
1  0.468090  0.449808   0.468090  0.468090   
2  0.599810  0.606604   0.599810  0.599810   
3  0.615081  0.611907   0.615081  0.615081   
4  0.553343  0.564815   0.553343  0.553343   
5  0.633555  0.647055   0.633555  0.633555   
6  0.614275  0.620351   0.614275  0.614275   
7  0.596598  0.608903   0.596598  0.596598   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T23:10:29.253170 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.588, 0.636, 0.4538152610441767, 0.7871485943775101, 0.642570281124498], 'avgAccuracy': 0.6215068273092369, 'f1': [0.5981201219154798, 0.6383418964076859, 0.46192133097649907, 0.799904496356075, 0.6522380951499854], 'avgF1': 0.630105188161145, 'precision': [0.588, 0.636, 0.4538152610441767, 0.7871485943775101, 0.642570281124498], 'avgPrecision': 0.6215068273092369, 'recall': [0.588, 0.636, 0.4538152610441767, 0.7871485943775101, 0.642570281124498], 'avgRecall': 0.6215068273092369, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-28T23:10:36.236493 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.592, 0.628, 0.43373493975903615, 0.751004016064257, 0.6506024096385542], 'avgAccuracy': 0.6110682730923694, 'f1': [0.6025583455737991, 0.6422275032353206, 0.41843728543344344, 0.79542058273456, 0.6472634098714892], 'avgF1': 0.6211814253697224, 'precision': [0.592, 0.628, 0.43373493975903615, 0.751004016064257, 0.6506024096385542], 'avgPrecision': 0.6110682730923694, 'recall': [0.592, 0.628, 0.43373493975903615, 0.751004016064257, 0.6506024096385542], 'avgRecall': 0.6110682730923694, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 15, 'p': 2, 'weights': 'distance'}]}
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Processing Model: LogisticRegression
2021-05-28T23:10:51.133476 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.604, 0.656, 0.40963855421686746, 0.714859437751004, 0.6586345381526104], 'avgAccuracy': 0.6086265060240964, 'f1': [0.6044305702943835, 0.644156126750986, 0.39419583094281885, 0.7465326894623027, 0.6579565013299953], 'avgF1': 0.6094543437560973, 'precision': [0.604, 0.656, 0.40963855421686746, 0.714859437751004, 0.6586345381526104], 'avgPrecision': 0.6086265060240964, 'recall': [0.604, 0.656, 0.40963855421686746, 0.714859437751004, 0.6586345381526104], 'avgRecall': 0.6086265060240964, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T23:10:51.570529 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.588, 0.64, 0.40963855421686746, 0.7630522088353414, 0.6706827309236948], 'avgAccuracy': 0.6142746987951807, 'f1': [0.5819666666666667, 0.6195747780959591, 0.40173365603814315, 0.7599212077874768, 0.6500418310696436], 'avgF1': 0.6026476279315779, 'precision': [0.588, 0.64, 0.40963855421686746, 0.7630522088353414, 0.6706827309236948], 'avgPrecision': 0.6142746987951807, 'recall': [0.588, 0.64, 0.40963855421686746, 0.7630522088353414, 0.6706827309236948], 'avgRecall': 0.6142746987951807, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-28T23:10:59.388762 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.592, 0.496, 0.4538152610441767, 0.7469879518072289, 0.5060240963855421], 'avgAccuracy': 0.5589654618473895, 'f1': [0.5931245869144702, 0.5058552787621287, 0.48476053565420374, 0.7676750742346156, 0.48208108482081086], 'avgF1': 0.5666993120772458, 'precision': [0.592, 0.496, 0.4538152610441767, 0.7469879518072289, 0.5060240963855421], 'avgPrecision': 0.5589654618473895, 'recall': [0.592, 0.496, 0.4538152610441767, 0.7469879518072289, 0.5060240963855421], 'avgRecall': 0.5589654618473895, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-28T23:11:00.366858 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.588, 0.628, 0.4538152610441767, 0.7831325301204819, 0.642570281124498], 'avgAccuracy': 0.6191036144578314, 'f1': [0.5981201219154798, 0.6422275032353206, 0.4632430552869379, 0.7966518908835662, 0.6511666311914183], 'avgF1': 0.6302818405025445, 'precision': [0.588, 0.628, 0.4538152610441767, 0.7831325301204819, 0.642570281124498], 'avgPrecision': 0.6191036144578314, 'recall': [0.588, 0.628, 0.4538152610441767, 0.7831325301204819, 0.642570281124498], 'avgRecall': 0.6191036144578314, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-28T23:11:02.690035 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.608, 0.652, 0.4457831325301205, 0.7269076305220884, 0.6666666666666666], 'avgAccuracy': 0.6198714859437751, 'f1': [0.6116425814420257, 0.6406507043708826, 0.454735101778283, 0.751004016064257, 0.6506574899597014], 'avgF1': 0.6217379787230299, 'precision': [0.608, 0.652, 0.4457831325301205, 0.7269076305220884, 0.6666666666666666], 'avgPrecision': 0.6198714859437751, 'recall': [0.608, 0.652, 0.4457831325301205, 0.7269076305220884, 0.6666666666666666], 'avgRecall': 0.6198714859437751, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T23:11:48.331344 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.604, 0.656, 0.40963855421686746, 0.7188755020080321, 0.6626506024096386], 'avgAccuracy': 0.6102329317269076, 'f1': [0.6044305702943835, 0.644156126750986, 0.39419583094281885, 0.7481713562364477, 0.645392222643601], 'avgF1': 0.6072692213736474, 'precision': [0.604, 0.656, 0.40963855421686746, 0.7188755020080321, 0.6626506024096386], 'avgPrecision': 0.6102329317269076, 'recall': [0.604, 0.656, 0.40963855421686746, 0.7188755020080321, 0.6626506024096386], 'avgRecall': 0.6102329317269076, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.588, 0.636, 0.4538152610441767, 0.7871485943775101, 0.642570281124498], 'avgAccuracy': 0.6215068273092369, 'f1': [0.5981201219154798, 0.6383418964076859, 0.46192133097649907, 0.799904496356075, 0.6522380951499854], 'avgF1': 0.630105188161145, 'precision': [0.588, 0.636, 0.4538152610441767, 0.7871485943775101, 0.642570281124498], 'avgPrecision': 0.6215068273092369, 'recall': [0.588, 0.636, 0.4538152610441767, 0.7871485943775101, 0.642570281124498], 'avgRecall': 0.6215068273092369, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.621507  0.629891   0.621507  0.621507   
1  0.468900  0.457183   0.468900  0.468900   
2  0.608627  0.609454   0.608627  0.608627   
3  0.614275  0.602648   0.614275  0.614275   
4  0.558965  0.566699   0.558965  0.558965   
5  0.611075  0.621001   0.611075  0.611075   
6  0.619871  0.621738   0.619871  0.619871   
7  0.605433  0.611825   0.605433  0.605433   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T23:19:03.414205 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.588, 0.636, 0.4497991967871486, 0.7751004016064257, 0.642570281124498], 'avgAccuracy': 0.6182939759036145, 'f1': [0.5981201219154798, 0.6383418964076859, 0.456825388042795, 0.7935340462975499, 0.6511666311914183], 'avgF1': 0.6275976167709858, 'precision': [0.588, 0.636, 0.4497991967871486, 0.7751004016064257, 0.642570281124498], 'avgPrecision': 0.6182939759036145, 'recall': [0.588, 0.636, 0.4497991967871486, 0.7751004016064257, 0.642570281124498], 'avgRecall': 0.6182939759036145, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-28T23:19:10.551790 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.592, 0.628, 0.42168674698795183, 0.7309236947791165, 0.6465863453815262], 'avgAccuracy': 0.6038393574297188, 'f1': [0.6025583455737991, 0.6422275032353206, 0.40042746029512305, 0.7803979658008805, 0.6443651014770254], 'avgF1': 0.6139952752764297, 'precision': [0.592, 0.628, 0.42168674698795183, 0.7309236947791165, 0.6465863453815262], 'avgPrecision': 0.6038393574297188, 'recall': [0.592, 0.628, 0.42168674698795183, 0.7309236947791165, 0.6465863453815262], 'avgRecall': 0.6038393574297188, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 15, 'p': 2, 'weights': 'distance'}]}
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Processing Model: LogisticRegression
2021-05-28T23:19:25.293114 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.596, 0.652, 0.40562248995983935, 0.6987951807228916, 0.6546184738955824], 'avgAccuracy': 0.6014072289156627, 'f1': [0.5966284993205202, 0.6406507043708826, 0.3907985526394286, 0.7301148650546242, 0.6544362763499401], 'avgF1': 0.6025257795470791, 'precision': [0.596, 0.652, 0.40562248995983935, 0.6987951807228916, 0.6546184738955824], 'avgPrecision': 0.6014072289156627, 'recall': [0.596, 0.652, 0.40562248995983935, 0.6987951807228916, 0.6546184738955824], 'avgRecall': 0.6014072289156627, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T23:19:25.652539 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.54, 0.632, 0.3855421686746988, 0.7590361445783133, 0.6706827309236948], 'avgAccuracy': 0.5974522088353413, 'f1': [0.5407923734957228, 0.6125443701717168, 0.3718364915574808, 0.7547033648005528, 0.6500418310696436], 'avgF1': 0.5859836862190234, 'precision': [0.54, 0.632, 0.3855421686746988, 0.7590361445783133, 0.6706827309236948], 'avgPrecision': 0.5974522088353413, 'recall': [0.54, 0.632, 0.3855421686746988, 0.7590361445783133, 0.6706827309236948], 'avgRecall': 0.5974522088353413, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-28T23:19:33.348393 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.6, 0.504, 0.4538152610441767, 0.7188755020080321, 0.5020080321285141], 'avgAccuracy': 0.5557397590361446, 'f1': [0.5924150197628457, 0.5149442877908903, 0.4856172262658005, 0.7460637637816944, 0.4829572548578424], 'avgF1': 0.5643995104918147, 'precision': [0.6, 0.504, 0.4538152610441767, 0.7188755020080321, 0.5020080321285141], 'avgPrecision': 0.5557397590361446, 'recall': [0.6, 0.504, 0.4538152610441767, 0.7188755020080321, 0.5020080321285141], 'avgRecall': 0.5557397590361446, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 100, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-28T23:19:34.499322 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.588, 0.624, 0.4457831325301205, 0.7831325301204819, 0.642570281124498], 'avgAccuracy': 0.6166971887550201, 'f1': [0.5981201219154798, 0.6377872128400056, 0.45312828556892154, 0.7979737497475938, 0.6511666311914183], 'avgF1': 0.6276352002526838, 'precision': [0.588, 0.624, 0.4457831325301205, 0.7831325301204819, 0.642570281124498], 'avgPrecision': 0.6166971887550201, 'recall': [0.588, 0.624, 0.4457831325301205, 0.7831325301204819, 0.642570281124498], 'avgRecall': 0.6166971887550201, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-28T23:19:36.824320 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.596, 0.652, 0.4457831325301205, 0.7269076305220884, 0.6666666666666666], 'avgAccuracy': 0.6174714859437751, 'f1': [0.5966284993205202, 0.6406507043708826, 0.45368038939634603, 0.751004016064257, 0.6481568327925875], 'avgF1': 0.6180240883889186, 'precision': [0.596, 0.652, 0.4457831325301205, 0.7269076305220884, 0.6666666666666666], 'avgPrecision': 0.6174714859437751, 'recall': [0.596, 0.652, 0.4457831325301205, 0.7269076305220884, 0.6666666666666666], 'avgRecall': 0.6174714859437751, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T23:20:26.718473 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.596, 0.652, 0.40562248995983935, 0.6987951807228916, 0.6626506024096386], 'avgAccuracy': 0.6030136546184739, 'f1': [0.5966284993205202, 0.6406507043708826, 0.3907985526394286, 0.7301148650546242, 0.6445356585601383], 'avgF1': 0.6005456559891188, 'precision': [0.596, 0.652, 0.40562248995983935, 0.6987951807228916, 0.6626506024096386], 'avgPrecision': 0.6030136546184739, 'recall': [0.596, 0.652, 0.40562248995983935, 0.6987951807228916, 0.6626506024096386], 'avgRecall': 0.6030136546184739, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.596, 0.652, 0.4457831325301205, 0.7269076305220884, 0.6666666666666666], 'avgAccuracy': 0.6174714859437751, 'f1': [0.5966284993205202, 0.6406507043708826, 0.45368038939634603, 0.751004016064257, 0.6481568327925875], 'avgF1': 0.6180240883889186, 'precision': [0.596, 0.652, 0.4457831325301205, 0.7269076305220884, 0.6666666666666666], 'avgPrecision': 0.6174714859437751, 'recall': [0.596, 0.652, 0.4457831325301205, 0.7269076305220884, 0.6666666666666666], 'avgRecall': 0.6174714859437751, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.616694  0.628375   0.616694  0.616694   
1  0.464884  0.453067   0.464884  0.464884   
2  0.601407  0.602526   0.601407  0.601407   
3  0.597452  0.585984   0.597452  0.597452   
4  0.554130  0.566673   0.554130  0.554130   
5  0.616697  0.627539   0.616697  0.616697   
6  0.617471  0.618024   0.617471  0.617471   
7  0.601410  0.605284   0.601410  0.601410   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T23:27:20.313815 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.592, 0.632, 0.4457831325301205, 0.7590361445783133, 0.6506024096385542], 'avgAccuracy': 0.6158843373493976, 'f1': [0.6033218390804598, 0.6344738479868622, 0.4508677369896693, 0.7781013731466874, 0.6475269571201686], 'avgF1': 0.6228583508647695, 'precision': [0.592, 0.632, 0.4457831325301205, 0.7590361445783133, 0.6506024096385542], 'avgPrecision': 0.6158843373493976, 'recall': [0.592, 0.632, 0.4457831325301205, 0.7590361445783133, 0.6506024096385542], 'avgRecall': 0.6158843373493976, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-28T23:27:27.499740 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.596, 0.628, 0.42168674698795183, 0.7188755020080321, 0.642570281124498], 'avgAccuracy': 0.6014265060240964, 'f1': [0.607280840862991, 0.6422275032353206, 0.3991324881317011, 0.7626871120847024, 0.641566265060241], 'avgF1': 0.6105788418749912, 'precision': [0.596, 0.628, 0.42168674698795183, 0.7188755020080321, 0.642570281124498], 'avgPrecision': 0.6014265060240964, 'recall': [0.596, 0.628, 0.42168674698795183, 0.7188755020080321, 0.642570281124498], 'avgRecall': 0.6014265060240964, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T23:27:42.085866 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.6, 0.644, 0.40562248995983935, 0.7068273092369478, 0.6546184738955824], 'avgAccuracy': 0.6022136546184739, 'f1': [0.6036935286935288, 0.6494525687406804, 0.3907985526394286, 0.7349397590361446, 0.6557713966948822], 'avgF1': 0.6069311611609329, 'precision': [0.6, 0.644, 0.40562248995983935, 0.7068273092369478, 0.6546184738955824], 'avgPrecision': 0.6022136546184739, 'recall': [0.6, 0.644, 0.40562248995983935, 0.7068273092369478, 0.6546184738955824], 'avgRecall': 0.6022136546184739, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-28T23:27:42.445239 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.58, 0.632, 0.3855421686746988, 0.7751004016064257, 0.6706827309236948], 'avgAccuracy': 0.6086650602409639, 'f1': [0.5744997626957759, 0.6129490392648288, 0.37085733257428016, 0.7694397643343823, 0.6489733154033833], 'avgF1': 0.5953438428545301, 'precision': [0.58, 0.632, 0.3855421686746988, 0.7751004016064257, 0.6706827309236948], 'avgPrecision': 0.6086650602409639, 'recall': [0.58, 0.632, 0.3855421686746988, 0.7751004016064257, 0.6706827309236948], 'avgRecall': 0.6086650602409639, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-28T23:27:50.623262 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.612, 0.64, 0.43775100401606426, 0.7429718875502008, 0.5662650602409639], 'avgAccuracy': 0.5997975903614458, 'f1': [0.6184414395672976, 0.6383344466042418, 0.44225235845596444, 0.7624044565358207, 0.5579326091469192], 'avgF1': 0.6038730620620487, 'precision': [0.612, 0.64, 0.43775100401606426, 0.7429718875502008, 0.5662650602409639], 'avgPrecision': 0.5997975903614458, 'recall': [0.612, 0.64, 0.43775100401606426, 0.7429718875502008, 0.5662650602409639], 'avgRecall': 0.5997975903614458, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-28T23:27:51.905267 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.592, 0.628, 0.4538152610441767, 0.7590361445783133, 0.6506024096385542], 'avgAccuracy': 0.6166907630522088, 'f1': [0.6033218390804598, 0.6294061821219716, 0.46274923579984995, 0.7781013731466874, 0.6475269571201686], 'avgF1': 0.6242211174538275, 'precision': [0.592, 0.628, 0.4538152610441767, 0.7590361445783133, 0.6506024096385542], 'avgPrecision': 0.6166907630522088, 'recall': [0.592, 0.628, 0.4538152610441767, 0.7590361445783133, 0.6506024096385542], 'avgRecall': 0.6166907630522088, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T23:27:54.290181 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.6, 0.648, 0.40963855421686746, 0.7269076305220884, 0.6666666666666666], 'avgAccuracy': 0.6102425702811245, 'f1': [0.6036935286935288, 0.6371394795299579, 0.39705166045708934, 0.751004016064257, 0.6506574899597014], 'avgF1': 0.6079092349409069, 'precision': [0.6, 0.648, 0.40963855421686746, 0.7269076305220884, 0.6666666666666666], 'avgPrecision': 0.6102425702811245, 'recall': [0.6, 0.648, 0.40963855421686746, 0.7269076305220884, 0.6666666666666666], 'avgRecall': 0.6102425702811245, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-28T23:28:42.602571 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.604, 0.644, 0.40562248995983935, 0.7108433734939759, 0.6546184738955824], 'avgAccuracy': 0.6038168674698795, 'f1': [0.6097664328009156, 0.6494525687406804, 0.3907985526394286, 0.7392137356766516, 0.6557713966948822], 'avgF1': 0.6090005373105116, 'precision': [0.604, 0.644, 0.40562248995983935, 0.7108433734939759, 0.6546184738955824], 'avgPrecision': 0.6038168674698795, 'recall': [0.604, 0.644, 0.40562248995983935, 0.7108433734939759, 0.6546184738955824], 'avgRecall': 0.6038168674698795, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.592, 0.628, 0.4538152610441767, 0.7590361445783133, 0.6506024096385542], 'avgAccuracy': 0.6166907630522088, 'f1': [0.6033218390804598, 0.6294061821219716, 0.46274923579984995, 0.7781013731466874, 0.6475269571201686], 'avgF1': 0.6242211174538275, 'precision': [0.592, 0.628, 0.4538152610441767, 0.7590361445783133, 0.6506024096385542], 'avgPrecision': 0.6166907630522088, 'recall': [0.592, 0.628, 0.4538152610441767, 0.7590361445783133, 0.6506024096385542], 'avgRecall': 0.6166907630522088, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.615081  0.621660   0.615081  0.615081   
1  0.460864  0.440906   0.460864  0.460864   
2  0.602214  0.606931   0.602214  0.602214   
3  0.608665  0.595344   0.608665  0.608665   
4  0.599798  0.603873   0.599798  0.599798   
5  0.613475  0.621126   0.613475  0.613475   
6  0.610243  0.607909   0.610243  0.610243   
7  0.603817  0.608985   0.603817  0.603817   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T23:35:33.475561 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.608, 0.648, 0.4738955823293173, 0.7590361445783133, 0.6666666666666666], 'avgAccuracy': 0.6311196787148594, 'f1': [0.6116425814420257, 0.634688426492588, 0.4983797187736231, 0.7781013731466874, 0.6511147592779555], 'avgF1': 0.6347853718265759, 'precision': [0.608, 0.648, 0.4738955823293173, 0.7590361445783133, 0.6666666666666666], 'avgPrecision': 0.6311196787148594, 'recall': [0.608, 0.648, 0.4738955823293173, 0.7590361445783133, 0.6666666666666666], 'avgRecall': 0.6311196787148594, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T23:35:40.514265 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.524, 0.648, 0.4779116465863454, 0.7309236947791165, 0.6626506024096386], 'avgAccuracy': 0.6086971887550201, 'f1': [0.5269224611866121, 0.652914821737252, 0.48534002805850224, 0.7755728797543114, 0.6474717050018256], 'avgF1': 0.6176443791477007, 'precision': [0.524, 0.648, 0.4779116465863454, 0.7309236947791165, 0.6626506024096386], 'avgPrecision': 0.6086971887550201, 'recall': [0.524, 0.648, 0.4779116465863454, 0.7309236947791165, 0.6626506024096386], 'avgRecall': 0.6086971887550201, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T23:35:55.413909 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.592, 0.612, 0.42971887550200805, 0.7188755020080321, 0.6465863453815262], 'avgAccuracy': 0.5998361445783132, 'f1': [0.5982050258684406, 0.6213139091237966, 0.4333705168394917, 0.7475306631933137, 0.6506173930893329], 'avgF1': 0.6102075016228751, 'precision': [0.592, 0.612, 0.42971887550200805, 0.7188755020080321, 0.6465863453815262], 'avgPrecision': 0.5998361445783132, 'recall': [0.592, 0.612, 0.42971887550200805, 0.7188755020080321, 0.6465863453815262], 'avgRecall': 0.5998361445783132, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T23:35:55.789308 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.584, 0.648, 0.2931726907630522, 0.6385542168674698, 0.6385542168674698], 'avgAccuracy': 0.5604562248995983, 'f1': [0.588440874592975, 0.6371394795299579, 0.16311156969709023, 0.6463379290357306, 0.6257200318036917], 'avgF1': 0.5321499769318891, 'precision': [0.584, 0.648, 0.2931726907630522, 0.6385542168674698, 0.6385542168674698], 'avgPrecision': 0.5604562248995983, 'recall': [0.584, 0.648, 0.2931726907630522, 0.6385542168674698, 0.6385542168674698], 'avgRecall': 0.5604562248995983, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T23:36:03.490393 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.612, 0.648, 0.46987951807228917, 0.7550200803212851, 0.5542168674698795], 'avgAccuracy': 0.6078232931726908, 'f1': [0.6175520476208417, 0.652914821737252, 0.49275029982726315, 0.7727603326692597, 0.5481033195763083], 'avgF1': 0.616816164286185, 'precision': [0.612, 0.648, 0.46987951807228917, 0.7550200803212851, 0.5542168674698795], 'avgPrecision': 0.6078232931726908, 'recall': [0.612, 0.648, 0.46987951807228917, 0.7550200803212851, 0.5542168674698795], 'avgRecall': 0.6078232931726908, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T23:36:04.473351 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.608, 0.648, 0.4859437751004016, 0.7590361445783133, 0.6666666666666666], 'avgAccuracy': 0.6335293172690764, 'f1': [0.6116425814420257, 0.634688426492588, 0.5151214959298356, 0.7959763708573908, 0.6501564780110155], 'avgF1': 0.6415170705465711, 'precision': [0.608, 0.648, 0.4859437751004016, 0.7590361445783133, 0.6666666666666666], 'avgPrecision': 0.6335293172690764, 'recall': [0.608, 0.648, 0.4859437751004016, 0.7590361445783133, 0.6666666666666666], 'avgRecall': 0.6335293172690764, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC
2021-05-28T23:36:06.901641 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.608, 0.652, 0.2931726907630522, 0.7269076305220884, 0.6666666666666666], 'avgAccuracy': 0.5893493975903614, 'f1': [0.6116425814420257, 0.6406507043708826, 0.16311156969709023, 0.751004016064257, 0.6501564780110155], 'avgF1': 0.5633130699170542, 'precision': [0.608, 0.652, 0.2931726907630522, 0.7269076305220884, 0.6666666666666666], 'avgPrecision': 0.5893493975903614, 'recall': [0.608, 0.652, 0.2931726907630522, 0.7269076305220884, 0.6666666666666666], 'avgRecall': 0.5893493975903614, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T23:36:57.249844 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.592, 0.612, 0.42971887550200805, 0.714859437751004, 0.6546184738955824], 'avgAccuracy': 0.6006393574297189, 'f1': [0.5982050258684406, 0.6213139091237966, 0.4333705168394917, 0.7434100036509674, 0.6557713966948822], 'avgF1': 0.6104141704355157, 'precision': [0.592, 0.612, 0.42971887550200805, 0.714859437751004, 0.6546184738955824], 'avgPrecision': 0.6006393574297189, 'recall': [0.592, 0.612, 0.42971887550200805, 0.714859437751004, 0.6546184738955824], 'avgRecall': 0.6006393574297189, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.608, 0.648, 0.4859437751004016, 0.7590361445783133, 0.6666666666666666], 'avgAccuracy': 0.6335293172690764, 'f1': [0.6116425814420257, 0.634688426492588, 0.5151214959298356, 0.7959763708573908, 0.6501564780110155], 'avgF1': 0.6415170705465711, 'precision': [0.608, 0.648, 0.4859437751004016, 0.7590361445783133, 0.6666666666666666], 'avgPrecision': 0.6335293172690764, 'recall': [0.608, 0.648, 0.4859437751004016, 0.7590361445783133, 0.6666666666666666], 'avgRecall': 0.6335293172690764, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.629513  0.637162   0.629513  0.629513   
1  0.452803  0.442016   0.452803  0.452803   
2  0.599836  0.610208   0.599836  0.599836   
3  0.560456  0.532150   0.560456  0.560456   
4  0.607823  0.616816   0.607823  0.607823   
5  0.631920  0.639618   0.631920  0.631920   
6  0.589349  0.563313   0.589349  0.589349   
7  0.599833  0.610504   0.599833  0.599833   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T23:43:45.514636 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.608, 0.652, 0.4819277108433735, 0.8072289156626506, 0.6626506024096386], 'avgAccuracy': 0.6423614457831326, 'f1': [0.6116425814420257, 0.6406507043708826, 0.5095644272281226, 0.8105718110537388, 0.6474742500877714], 'avgF1': 0.6439807548365082, 'precision': [0.608, 0.652, 0.4819277108433735, 0.8072289156626506, 0.6626506024096386], 'avgPrecision': 0.6423614457831326, 'recall': [0.608, 0.652, 0.4819277108433735, 0.8072289156626506, 0.6626506024096386], 'avgRecall': 0.6423614457831326, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T23:43:52.585387 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.52, 0.652, 0.46586345381526106, 0.7911646586345381, 0.6626506024096386], 'avgAccuracy': 0.6183357429718875, 'f1': [0.5219916142557651, 0.6406507043708826, 0.48709498356712916, 0.8051952846127631, 0.6465306786012759], 'avgF1': 0.6202926530815631, 'precision': [0.52, 0.652, 0.46586345381526106, 0.7911646586345381, 0.6626506024096386], 'avgPrecision': 0.6183357429718875, 'recall': [0.52, 0.652, 0.46586345381526106, 0.7911646586345381, 0.6626506024096386], 'avgRecall': 0.6183357429718875, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T23:44:07.186987 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.588, 0.616, 0.42971887550200805, 0.7670682730923695, 0.6586345381526104], 'avgAccuracy': 0.6118843373493976, 'f1': [0.5944057833791824, 0.6087414978791758, 0.4333705168394917, 0.7757428224873455, 0.6460164071295604], 'avgF1': 0.6116554055429512, 'precision': [0.588, 0.616, 0.42971887550200805, 0.7670682730923695, 0.6586345381526104], 'avgPrecision': 0.6118843373493976, 'recall': [0.588, 0.616, 0.42971887550200805, 0.7670682730923695, 0.6586345381526104], 'avgRecall': 0.6118843373493976, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T23:44:07.546405 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.588, 0.612, 0.42168674698795183, 0.7791164658634538, 0.6546184738955824], 'avgAccuracy': 0.6110843373493976, 'f1': [0.5941312373598087, 0.6051415129837867, 0.42145574605162966, 0.7801475051344093, 0.6442144306573013], 'avgF1': 0.6090180864373871, 'precision': [0.588, 0.612, 0.42168674698795183, 0.7791164658634538, 0.6546184738955824], 'avgPrecision': 0.6110843373493976, 'recall': [0.588, 0.612, 0.42168674698795183, 0.7791164658634538, 0.6546184738955824], 'avgRecall': 0.6110843373493976, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T23:44:15.435602 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.608, 0.652, 0.46987951807228917, 0.8112449799196787, 0.5622489959839357], 'avgAccuracy': 0.6206746987951808, 'f1': [0.6116425814420257, 0.6406507043708826, 0.49275029982726315, 0.8116386024913339, 0.5528060097216084], 'avgF1': 0.6218976395706227, 'precision': [0.608, 0.652, 0.46987951807228917, 0.8112449799196787, 0.5622489959839357], 'avgPrecision': 0.6206746987951808, 'recall': [0.608, 0.652, 0.46987951807228917, 0.8112449799196787, 0.5622489959839357], 'avgRecall': 0.6206746987951808, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T23:44:16.381728 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.608, 0.652, 0.4899598393574297, 0.8112449799196787, 0.6626506024096386], 'avgAccuracy': 0.6447710843373494, 'f1': [0.6116425814420257, 0.6406507043708826, 0.520656226824397, 0.8163549435838593, 0.6494692628752395], 'avgF1': 0.6477547438192808, 'precision': [0.608, 0.652, 0.4899598393574297, 0.8112449799196787, 0.6626506024096386], 'avgPrecision': 0.6447710843373494, 'recall': [0.608, 0.652, 0.4899598393574297, 0.8112449799196787, 0.6626506024096386], 'avgRecall': 0.6447710843373494, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC
2021-05-28T23:44:18.568340 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.608, 0.652, 0.2931726907630522, 0.7831325301204819, 0.6706827309236948], 'avgAccuracy': 0.6013975903614458, 'f1': [0.6116425814420257, 0.6406507043708826, 0.16311156969709023, 0.7850140963238514, 0.6528429186276736], 'avgF1': 0.5706523740923047, 'precision': [0.608, 0.652, 0.2931726907630522, 0.7831325301204819, 0.6706827309236948], 'avgPrecision': 0.6013975903614458, 'recall': [0.608, 0.652, 0.2931726907630522, 0.7831325301204819, 0.6706827309236948], 'avgRecall': 0.6013975903614458, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T23:45:04.844777 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.592, 0.616, 0.42971887550200805, 0.7710843373493976, 0.6546184738955824], 'avgAccuracy': 0.6126843373493976, 'f1': [0.5982050258684406, 0.6087414978791758, 0.4333705168394917, 0.7806094136767876, 0.6432898580875335], 'avgF1': 0.6128432624702859, 'precision': [0.592, 0.616, 0.42971887550200805, 0.7710843373493976, 0.6546184738955824], 'avgPrecision': 0.6126843373493976, 'recall': [0.592, 0.616, 0.42971887550200805, 0.7710843373493976, 0.6546184738955824], 'avgRecall': 0.6126843373493976, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.608, 0.652, 0.4899598393574297, 0.8112449799196787, 0.6626506024096386], 'avgAccuracy': 0.6447710843373494, 'f1': [0.6116425814420257, 0.6406507043708826, 0.520656226824397, 0.8163549435838593, 0.6494692628752395], 'avgF1': 0.6477547438192808, 'precision': [0.608, 0.652, 0.4899598393574297, 0.8112449799196787, 0.6626506024096386], 'avgPrecision': 0.6447710843373494, 'recall': [0.608, 0.652, 0.4899598393574297, 0.8112449799196787, 0.6626506024096386], 'avgRecall': 0.6447710843373494, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

                    model                                      features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch   
6                     SVC  Active inflammation?, Severity of Crypt Arch   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch   

   accuracy        f1  precision    recall  \
0  0.640755  0.641744   0.640755  0.640755   
1  0.586137  0.583885   0.586137  0.586137   
2  0.611884  0.611906   0.611884  0.611884   
3  0.611084  0.609018   0.611084  0.611084   
4  0.620675  0.621898   0.620675  0.620675   
5  0.642361  0.643921   0.642361  0.642361   
6  0.601398  0.570652   0.601398  0.601398   
7  0.612684  0.613028   0.612684  0.612684   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-28T23:51:29.627412 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgAccuracy': 0.5348530120481928, 'f1': [0.4562137637692224, 0.5376398451739745, 0.13682850563190535, 0.651913352893439, 0.5585310275711224], 'avgF1': 0.4682252990079327, 'precision': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgPrecision': 0.5348530120481928, 'recall': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgRecall': 0.5348530120481928, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-28T23:51:36.483250 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.15261044176706828], 'avgAccuracy': 0.4424835341365462, 'f1': [0.4562137637692224, 0.5376398451739745, 0.13682850563190535, 0.651913352893439, 0.040412521164798564], 'avgF1': 0.36460159772666795, 'precision': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.15261044176706828], 'avgPrecision': 0.4424835341365462, 'recall': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.15261044176706828], 'avgRecall': 0.4424835341365462, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-28T23:51:50.277830 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgAccuracy': 0.5348530120481928, 'f1': [0.4562137637692224, 0.5376398451739745, 0.13682850563190535, 0.651913352893439, 0.5585310275711224], 'avgF1': 0.4682252990079327, 'precision': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgPrecision': 0.5348530120481928, 'recall': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgRecall': 0.5348530120481928, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-28T23:51:50.699706 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgAccuracy': 0.5348530120481928, 'f1': [0.4562137637692224, 0.5376398451739745, 0.13682850563190535, 0.651913352893439, 0.5585310275711224], 'avgF1': 0.4682252990079327, 'precision': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgPrecision': 0.5348530120481928, 'recall': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgRecall': 0.5348530120481928, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-28T23:51:58.442896 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgAccuracy': 0.5348530120481928, 'f1': [0.4562137637692224, 0.5376398451739745, 0.13682850563190535, 0.651913352893439, 0.5585310275711224], 'avgF1': 0.4682252990079327, 'precision': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgPrecision': 0.5348530120481928, 'recall': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgRecall': 0.5348530120481928, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-28T23:51:59.474295 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgAccuracy': 0.5348530120481928, 'f1': [0.4562137637692224, 0.5376398451739745, 0.13682850563190535, 0.651913352893439, 0.5585310275711224], 'avgF1': 0.4682252990079327, 'precision': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgPrecision': 0.5348530120481928, 'recall': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgRecall': 0.5348530120481928, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-28T23:52:01.626342 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgAccuracy': 0.5348530120481928, 'f1': [0.4562137637692224, 0.5376398451739745, 0.13682850563190535, 0.651913352893439, 0.5585310275711224], 'avgF1': 0.4682252990079327, 'precision': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgPrecision': 0.5348530120481928, 'recall': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgRecall': 0.5348530120481928, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-28T23:53:24.808900 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgAccuracy': 0.5348530120481928, 'f1': [0.4562137637692224, 0.5376398451739745, 0.13682850563190535, 0.651913352893439, 0.5585310275711224], 'avgF1': 0.4682252990079327, 'precision': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgPrecision': 0.5348530120481928, 'recall': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgRecall': 0.5348530120481928, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgAccuracy': 0.5348530120481928, 'f1': [0.4562137637692224, 0.5376398451739745, 0.13682850563190535, 0.651913352893439, 0.5585310275711224], 'avgF1': 0.4682252990079327, 'precision': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgPrecision': 0.5348530120481928, 'recall': [0.508, 0.6, 0.24096385542168675, 0.7108433734939759, 0.6144578313253012], 'avgRecall': 0.5348530120481928, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model              features  accuracy        f1  \
0  RandomForestClassifier  Active inflammation?  0.534853  0.468225   
1    KNeighborsClassifier  Active inflammation?  0.300315  0.234219   
2      LogisticRegression  Active inflammation?  0.534853  0.468225   
3              GaussianNB  Active inflammation?  0.534853  0.468225   
4      AdaBoostClassifier  Active inflammation?  0.534853  0.468225   
5  DecisionTreeClassifier  Active inflammation?  0.534853  0.468225   
6                     SVC  Active inflammation?  0.534853  0.468225   
7           MLPClassifier  Active inflammation?  0.534853  0.468225   

   precision    recall                                             params  
0   0.534853  0.534853  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1   0.300315  0.300315  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2   0.534853  0.534853  {'C': 1, 'class_weight': None, 'dual': False, ...  
3   0.534853  0.534853           {'priors': None, 'var_smoothing': 1e-09}  
4   0.534853  0.534853  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5   0.534853  0.534853  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6   0.534853  0.534853  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7   0.534853  0.534853  {'activation': 'logistic', 'alpha': 0.0001, 'b...
In [93]:
# Original Dataset
X2 = pd.concat([X_train_ord, X_test_ord]) #.to_numpy()
y2 = pd.concat([y_train_ord, y_test_ord]).to_numpy()
#data2 = (X2, y2, n_folds)

print('********************************************')
print('Starting Original data set....')
print('********************************************')

for i in range(l ,0, -1):
    col = []
    col = df[:i]
    nX2 = X2.loc[:, col]
    nX2 = nX2.to_numpy()
    data2 = (nX2, y2, n_folds)
    hyper_search(modelDictionary, modelParamsDictionary, data2, col)

********************************************
Starting Original data set....
********************************************

Processing Model: RandomForestClassifier
2021-05-29T00:00:30.657195 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.8518518518518519, 0.8198757763975155], 'avgAccuracy': 0.8096541676251822, 'f1': [0.7925585561610762, 0.7492156623069715, 0.7892421500929311, 0.8431364129944399, 0.8113240972915401], 'avgF1': 0.7970953757693917, 'precision': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.8518518518518519, 0.8198757763975155], 'avgPrecision': 0.8096541676251822, 'recall': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.8518518518518519, 0.8198757763975155], 'avgRecall': 0.8096541676251822, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T00:00:37.859421 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.7160493827160493, 0.6851851851851852, 0.7345679012345679, 0.7716049382716049, 0.7391304347826086], 'avgAccuracy': 0.7293075684380033, 'f1': [0.6761890996305074, 0.6616916947214572, 0.7107772995058727, 0.7556209913298924, 0.7203451908649506], 'avgF1': 0.704924855210536, 'precision': [0.7160493827160493, 0.6851851851851852, 0.7345679012345679, 0.7716049382716049, 0.7391304347826086], 'avgPrecision': 0.7293075684380033, 'recall': [0.7160493827160493, 0.6851851851851852, 0.7345679012345679, 0.7716049382716049, 0.7391304347826086], 'avgRecall': 0.7293075684380033, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
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Processing Model: LogisticRegression
2021-05-29T00:00:53.647714 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.7283950617283951, 0.7160493827160493, 0.7283950617283951, 0.7901234567901234, 0.7701863354037267], 'avgAccuracy': 0.746629859673338, 'f1': [0.699833174459488, 0.6810868739794413, 0.7008875978374889, 0.7560217746582957, 0.7507086953354385], 'avgF1': 0.7177076232540305, 'precision': [0.7283950617283951, 0.7160493827160493, 0.7283950617283951, 0.7901234567901234, 0.7701863354037267], 'avgPrecision': 0.746629859673338, 'recall': [0.7283950617283951, 0.7160493827160493, 0.7283950617283951, 0.7901234567901234, 0.7701863354037267], 'avgRecall': 0.746629859673338, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-29T00:00:54.319741 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgAccuracy': 0.6279349743117859, 'f1': [0.5366536949203194, 0.5779291112946431, 0.6045764195869905, 0.6616356136219576, 0.6091665431317664], 'avgF1': 0.5979922765111354, 'precision': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgPrecision': 0.6279349743117859, 'recall': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgRecall': 0.6279349743117859, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-29T00:01:01.193404 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.7777777777777778, 0.7345679012345679, 0.7222222222222222, 0.7654320987654321, 0.8136645962732919], 'avgAccuracy': 0.7627329192546584, 'f1': [0.756874065884569, 0.6934539656660759, 0.7033772092448399, 0.7501131004431336, 0.7916305090790217], 'avgF1': 0.739089770063528, 'precision': [0.7777777777777778, 0.7345679012345679, 0.7222222222222222, 0.7654320987654321, 0.8136645962732919], 'avgPrecision': 0.7627329192546584, 'recall': [0.7777777777777778, 0.7345679012345679, 0.7222222222222222, 0.7654320987654321, 0.8136645962732919], 'avgRecall': 0.7627329192546584, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-29T00:01:02.035991 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.7469135802469136, 0.6975308641975309, 0.7530864197530864, 0.7345679012345679, 0.7329192546583851], 'avgAccuracy': 0.7330036040180968, 'f1': [0.740944611718911, 0.6973110976040459, 0.7528308228818723, 0.7307201663793479, 0.7400675963682595], 'avgF1': 0.7323748589904873, 'precision': [0.7469135802469136, 0.6975308641975309, 0.7530864197530864, 0.7345679012345679, 0.7329192546583851], 'avgPrecision': 0.7330036040180968, 'recall': [0.7469135802469136, 0.6975308641975309, 0.7530864197530864, 0.7345679012345679, 0.7329192546583851], 'avgRecall': 0.7330036040180968, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-29T00:01:03.914446 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.691358024691358, 0.7283950617283951, 0.7160493827160493, 0.8024691358024691, 0.7515527950310559], 'avgAccuracy': 0.7379648799938655, 'f1': [0.6299816852700939, 0.6569959308513009, 0.6590775208372501, 0.7615781646724789, 0.7130895263504133], 'avgF1': 0.6841445655963074, 'precision': [0.691358024691358, 0.7283950617283951, 0.7160493827160493, 0.8024691358024691, 0.7515527950310559], 'avgPrecision': 0.7379648799938655, 'recall': [0.691358024691358, 0.7283950617283951, 0.7160493827160493, 0.8024691358024691, 0.7515527950310559], 'avgRecall': 0.7379648799938655, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-29T00:04:19.146173 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.7283950617283951, 0.7222222222222222, 0.7098765432098766, 0.808641975308642, 0.7701863354037267], 'avgAccuracy': 0.7478644275745725, 'f1': [0.7000456342253106, 0.6944224857268335, 0.6881572930955648, 0.7858978241150171, 0.7456370597422673], 'avgF1': 0.7228320593809987, 'precision': [0.7283950617283951, 0.7222222222222222, 0.7098765432098766, 0.808641975308642, 0.7701863354037267], 'avgPrecision': 0.7478644275745725, 'recall': [0.7283950617283951, 0.7222222222222222, 0.7098765432098766, 0.808641975308642, 0.7701863354037267], 'avgRecall': 0.7478644275745725, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year, Subepithelial collagen', 'accuracy': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.8518518518518519, 0.8198757763975155], 'avgAccuracy': 0.8096541676251822, 'f1': [0.7925585561610762, 0.7492156623069715, 0.7892421500929311, 0.8431364129944399, 0.8113240972915401], 'avgF1': 0.7970953757693917, 'precision': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.8518518518518519, 0.8198757763975155], 'avgPrecision': 0.8096541676251822, 'recall': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.8518518518518519, 0.8198757763975155], 'avgRecall': 0.8096541676251822, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.792363  0.775248   0.792363  0.792363   
1  0.724362  0.690340   0.724362  0.724362   
2  0.746630  0.717708   0.746630  0.746630   
3  0.627935  0.597992   0.627935  0.627935   
4  0.704563  0.708784   0.704563  0.704563   
5  0.660087  0.660704   0.660087  0.660087   
6  0.737965  0.684145   0.737965  0.737965   
7  0.736715  0.704336   0.736715  0.736715   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T00:11:14.849524 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.845679012345679, 0.8260869565217391], 'avgAccuracy': 0.8096618357487922, 'f1': [0.7930015953044162, 0.7461158814294129, 0.7853188209102707, 0.8353409533432268, 0.8167648594227758], 'avgF1': 0.7953084220820205, 'precision': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.845679012345679, 0.8260869565217391], 'avgPrecision': 0.8096618357487922, 'recall': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.845679012345679, 0.8260869565217391], 'avgRecall': 0.8096618357487922, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T00:11:21.237761 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.7160493827160493, 0.6851851851851852, 0.7345679012345679, 0.7716049382716049, 0.7391304347826086], 'avgAccuracy': 0.7293075684380033, 'f1': [0.6761890996305074, 0.6616916947214572, 0.7107772995058727, 0.7556209913298924, 0.7203451908649506], 'avgF1': 0.704924855210536, 'precision': [0.7160493827160493, 0.6851851851851852, 0.7345679012345679, 0.7716049382716049, 0.7391304347826086], 'avgPrecision': 0.7293075684380033, 'recall': [0.7160493827160493, 0.6851851851851852, 0.7345679012345679, 0.7716049382716049, 0.7391304347826086], 'avgRecall': 0.7293075684380033, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T00:11:35.130544 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.7283950617283951, 0.7160493827160493, 0.7283950617283951, 0.7901234567901234, 0.7701863354037267], 'avgAccuracy': 0.746629859673338, 'f1': [0.699833174459488, 0.6810868739794413, 0.7008875978374889, 0.7560217746582957, 0.7507086953354385], 'avgF1': 0.7177076232540305, 'precision': [0.7283950617283951, 0.7160493827160493, 0.7283950617283951, 0.7901234567901234, 0.7701863354037267], 'avgPrecision': 0.746629859673338, 'recall': [0.7283950617283951, 0.7160493827160493, 0.7283950617283951, 0.7901234567901234, 0.7701863354037267], 'avgRecall': 0.746629859673338, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T00:11:35.516859 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgAccuracy': 0.6279349743117859, 'f1': [0.5366536949203194, 0.5779291112946431, 0.6045764195869905, 0.6616356136219576, 0.6091665431317664], 'avgF1': 0.5979922765111354, 'precision': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgPrecision': 0.6279349743117859, 'recall': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgRecall': 0.6279349743117859, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T00:11:41.220653 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.7777777777777778, 0.7345679012345679, 0.7222222222222222, 0.7654320987654321, 0.8136645962732919], 'avgAccuracy': 0.7627329192546584, 'f1': [0.756874065884569, 0.6934539656660759, 0.7033772092448399, 0.7501131004431336, 0.7916305090790217], 'avgF1': 0.739089770063528, 'precision': [0.7777777777777778, 0.7345679012345679, 0.7222222222222222, 0.7654320987654321, 0.8136645962732919], 'avgPrecision': 0.7627329192546584, 'recall': [0.7777777777777778, 0.7345679012345679, 0.7222222222222222, 0.7654320987654321, 0.8136645962732919], 'avgRecall': 0.7627329192546584, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-29T00:11:41.957216 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.691358024691358, 0.7407407407407407, 0.7592592592592593, 0.7037037037037037, 0.7018633540372671], 'avgAccuracy': 0.7193850164864658, 'f1': [0.6922722606625714, 0.7349820865406766, 0.7555820170996322, 0.7090943002544247, 0.7099496517974779], 'avgF1': 0.7203760632709566, 'precision': [0.691358024691358, 0.7407407407407407, 0.7592592592592593, 0.7037037037037037, 0.7018633540372671], 'avgPrecision': 0.7193850164864658, 'recall': [0.691358024691358, 0.7407407407407407, 0.7592592592592593, 0.7037037037037037, 0.7018633540372671], 'avgRecall': 0.7193850164864658, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-29T00:11:43.270570 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.691358024691358, 0.7283950617283951, 0.7160493827160493, 0.8024691358024691, 0.7515527950310559], 'avgAccuracy': 0.7379648799938655, 'f1': [0.6299816852700939, 0.6569959308513009, 0.6590775208372501, 0.7615781646724789, 0.7130895263504133], 'avgF1': 0.6841445655963074, 'precision': [0.691358024691358, 0.7283950617283951, 0.7160493827160493, 0.8024691358024691, 0.7515527950310559], 'avgPrecision': 0.7379648799938655, 'recall': [0.691358024691358, 0.7283950617283951, 0.7160493827160493, 0.8024691358024691, 0.7515527950310559], 'avgRecall': 0.7379648799938655, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-29T00:14:51.094864 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.7283950617283951, 0.7160493827160493, 0.7469135802469136, 0.8024691358024691, 0.782608695652174], 'avgAccuracy': 0.7552871712292002, 'f1': [0.7000456342253106, 0.6850049270555436, 0.7222442287801766, 0.7801126508580241, 0.7618387753744603], 'avgF1': 0.729849243258703, 'precision': [0.7283950617283951, 0.7160493827160493, 0.7469135802469136, 0.8024691358024691, 0.782608695652174], 'avgPrecision': 0.7552871712292002, 'recall': [0.7283950617283951, 0.7160493827160493, 0.7469135802469136, 0.8024691358024691, 0.782608695652174], 'avgRecall': 0.7552871712292002, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
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*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.845679012345679, 0.8260869565217391], 'avgAccuracy': 0.8096618357487922, 'f1': [0.7930015953044162, 0.7461158814294129, 0.7853188209102707, 0.8353409533432268, 0.8167648594227758], 'avgF1': 0.7953084220820205, 'precision': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.845679012345679, 0.8260869565217391], 'avgPrecision': 0.8096618357487922, 'recall': [0.8024691358024691, 0.7716049382716049, 0.8024691358024691, 0.845679012345679, 0.8260869565217391], 'avgRecall': 0.8096618357487922, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.798535  0.784459   0.798535  0.798535   
1  0.724362  0.690340   0.724362  0.724362   
2  0.746630  0.717708   0.746630  0.746630   
3  0.627935  0.597992   0.627935  0.627935   
4  0.704563  0.708784   0.704563  0.704563   
5  0.684802  0.686611   0.684802  0.684802   
6  0.737965  0.684145   0.737965  0.737965   
7  0.746615  0.723976   0.746615  0.746615   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T00:21:47.246714 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.808641975308642, 0.7654320987654321, 0.8024691358024691, 0.845679012345679, 0.8198757763975155], 'avgAccuracy': 0.8084195997239475, 'f1': [0.7962789483846832, 0.7446224668823539, 0.7964207217679439, 0.8302317840716533, 0.8184214166882456], 'avgF1': 0.797195067558976, 'precision': [0.808641975308642, 0.7654320987654321, 0.8024691358024691, 0.845679012345679, 0.8198757763975155], 'avgPrecision': 0.8084195997239475, 'recall': [0.808641975308642, 0.7654320987654321, 0.8024691358024691, 0.845679012345679, 0.8198757763975155], 'avgRecall': 0.8084195997239475, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-29T00:21:53.841941 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.7222222222222222, 0.7160493827160493, 0.7283950617283951, 0.7716049382716049, 0.7204968944099379], 'avgAccuracy': 0.7317536998696419, 'f1': [0.6898445358939187, 0.6915087066368815, 0.7003901073648613, 0.7489204985984375, 0.7084085449919695], 'avgF1': 0.7078144786972137, 'precision': [0.7222222222222222, 0.7160493827160493, 0.7283950617283951, 0.7716049382716049, 0.7204968944099379], 'avgPrecision': 0.7317536998696419, 'recall': [0.7222222222222222, 0.7160493827160493, 0.7283950617283951, 0.7716049382716049, 0.7204968944099379], 'avgRecall': 0.7317536998696419, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
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Processing Model: LogisticRegression
2021-05-29T00:22:07.338376 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.7345679012345679, 0.7037037037037037, 0.7345679012345679, 0.7777777777777778, 0.7639751552795031], 'avgAccuracy': 0.7429184878460241, 'f1': [0.7051953635701563, 0.6655425274520752, 0.6994138588405728, 0.7453680182231989, 0.7446147072890306], 'avgF1': 0.7120268950750067, 'precision': [0.7345679012345679, 0.7037037037037037, 0.7345679012345679, 0.7777777777777778, 0.7639751552795031], 'avgPrecision': 0.7429184878460241, 'recall': [0.7345679012345679, 0.7037037037037037, 0.7345679012345679, 0.7777777777777778, 0.7639751552795031], 'avgRecall': 0.7429184878460241, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-29T00:22:07.719311 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgAccuracy': 0.6279349743117859, 'f1': [0.5366536949203194, 0.5779291112946431, 0.6045764195869905, 0.6616356136219576, 0.6091665431317664], 'avgF1': 0.5979922765111354, 'precision': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgPrecision': 0.6279349743117859, 'recall': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgRecall': 0.6279349743117859, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-29T00:22:13.167964 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.7716049382716049, 0.7592592592592593, 0.6851851851851852, 0.7962962962962963, 0.7888198757763976], 'avgAccuracy': 0.7602331109577487, 'f1': [0.7519021354567755, 0.7433883968275503, 0.6638359326670793, 0.7637656532293443, 0.7676200677703383], 'avgF1': 0.7381024371902175, 'precision': [0.7716049382716049, 0.7592592592592593, 0.6851851851851852, 0.7962962962962963, 0.7888198757763976], 'avgPrecision': 0.7602331109577487, 'recall': [0.7716049382716049, 0.7592592592592593, 0.6851851851851852, 0.7962962962962963, 0.7888198757763976], 'avgRecall': 0.7602331109577487, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-29T00:22:13.902991 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.7098765432098766, 0.7160493827160493, 0.7283950617283951, 0.7530864197530864, 0.6894409937888198], 'avgAccuracy': 0.7193696802392454, 'f1': [0.706706101140463, 0.723823997374373, 0.7287349776828081, 0.7544786356284421, 0.703714986397374], 'avgF1': 0.723491739644692, 'precision': [0.7098765432098766, 0.7160493827160493, 0.7283950617283951, 0.7530864197530864, 0.6894409937888198], 'avgPrecision': 0.7193696802392454, 'recall': [0.7098765432098766, 0.7160493827160493, 0.7283950617283951, 0.7530864197530864, 0.6894409937888198], 'avgRecall': 0.7193696802392454, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T00:22:15.280930 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.6851851851851852, 0.7283950617283951, 0.7160493827160493, 0.8024691358024691, 0.7453416149068323], 'avgAccuracy': 0.7354880760677862, 'f1': [0.6247762365811498, 0.6567102977690645, 0.6587090548281654, 0.7615457491672244, 0.6973026947598235], 'avgF1': 0.6798088066210856, 'precision': [0.6851851851851852, 0.7283950617283951, 0.7160493827160493, 0.8024691358024691, 0.7453416149068323], 'avgPrecision': 0.7354880760677862, 'recall': [0.6851851851851852, 0.7283950617283951, 0.7160493827160493, 0.8024691358024691, 0.7453416149068323], 'avgRecall': 0.7354880760677862, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-29T00:25:27.141351 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.7098765432098766, 0.7222222222222222, 0.7407407407407407, 0.8209876543209876, 0.7701863354037267], 'avgAccuracy': 0.7528026991795108, 'f1': [0.6794688245668639, 0.686596415973897, 0.7173384499533737, 0.8041358310206599, 0.744897271345343], 'avgF1': 0.7264873585720275, 'precision': [0.7098765432098766, 0.7222222222222222, 0.7407407407407407, 0.8209876543209876, 0.7701863354037267], 'avgPrecision': 0.7528026991795108, 'recall': [0.7098765432098766, 0.7222222222222222, 0.7407407407407407, 0.8209876543209876, 0.7701863354037267], 'avgRecall': 0.7528026991795108, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.808641975308642, 0.7654320987654321, 0.8024691358024691, 0.845679012345679, 0.8198757763975155], 'avgAccuracy': 0.8084195997239475, 'f1': [0.7962789483846832, 0.7446224668823539, 0.7964207217679439, 0.8302317840716533, 0.8184214166882456], 'avgF1': 0.797195067558976, 'precision': [0.808641975308642, 0.7654320987654321, 0.8024691358024691, 0.845679012345679, 0.8198757763975155], 'avgPrecision': 0.8084195997239475, 'recall': [0.808641975308642, 0.7654320987654321, 0.8024691358024691, 0.845679012345679, 0.8198757763975155], 'avgRecall': 0.8084195997239475, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.793559  0.779351   0.793559  0.793559   
1  0.726823  0.691400   0.726823  0.726823   
2  0.740457  0.709019   0.740457  0.740457   
3  0.627935  0.597992   0.627935  0.627935   
4  0.713220  0.718220   0.713220  0.713220   
5  0.690952  0.690456   0.690952  0.690952   
6  0.735488  0.679809   0.735488  0.735488   
7  0.742903  0.714579   0.742903  0.742903   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T00:32:23.139554 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.7777777777777778, 0.7654320987654321, 0.7839506172839507, 0.808641975308642, 0.782608695652174], 'avgAccuracy': 0.7836822329575953, 'f1': [0.7649507429518596, 0.7489167991388527, 0.7811684126791516, 0.7980946596423862, 0.780555700541086], 'avgF1': 0.7747372629906673, 'precision': [0.7777777777777778, 0.7654320987654321, 0.7839506172839507, 0.808641975308642, 0.782608695652174], 'avgPrecision': 0.7836822329575953, 'recall': [0.7777777777777778, 0.7654320987654321, 0.7839506172839507, 0.808641975308642, 0.782608695652174], 'avgRecall': 0.7836822329575953, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T00:32:29.457985 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.7222222222222222, 0.7098765432098766, 0.7530864197530864, 0.8271604938271605, 0.7267080745341615], 'avgAccuracy': 0.7478107507093015, 'f1': [0.6944367300481468, 0.6873991112668157, 0.7321643933933605, 0.8088803361845122, 0.715383058287078], 'avgF1': 0.7276527258359826, 'precision': [0.7222222222222222, 0.7098765432098766, 0.7530864197530864, 0.8271604938271605, 0.7267080745341615], 'avgPrecision': 0.7478107507093015, 'recall': [0.7222222222222222, 0.7098765432098766, 0.7530864197530864, 0.8271604938271605, 0.7267080745341615], 'avgRecall': 0.7478107507093015, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T00:32:43.123245 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.7222222222222222, 0.7098765432098766, 0.7407407407407407, 0.7901234567901234, 0.7639751552795031], 'avgAccuracy': 0.7453876236484932, 'f1': [0.6908352445550514, 0.6752552467072781, 0.7036521859071018, 0.7577002366315833, 0.744047049133153], 'avgF1': 0.7142979925868335, 'precision': [0.7222222222222222, 0.7098765432098766, 0.7407407407407407, 0.7901234567901234, 0.7639751552795031], 'avgPrecision': 0.7453876236484932, 'recall': [0.7222222222222222, 0.7098765432098766, 0.7407407407407407, 0.7901234567901234, 0.7639751552795031], 'avgRecall': 0.7453876236484932, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T00:32:43.545852 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgAccuracy': 0.6279349743117859, 'f1': [0.5366536949203194, 0.5779291112946431, 0.6045764195869905, 0.6616356136219576, 0.6091665431317664], 'avgF1': 0.5979922765111354, 'precision': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgPrecision': 0.6279349743117859, 'recall': [0.5679012345679012, 0.6234567901234568, 0.6419753086419753, 0.6790123456790124, 0.6273291925465838], 'avgRecall': 0.6279349743117859, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T00:32:49.099842 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.8024691358024691, 0.7469135802469136, 0.7345679012345679, 0.7469135802469136, 0.7763975155279503], 'avgAccuracy': 0.7614523426117629, 'f1': [0.7965684474202145, 0.7216735759670174, 0.7363265766468338, 0.737310405643739, 0.7771715488744876], 'avgF1': 0.7538101109104585, 'precision': [0.8024691358024691, 0.7469135802469136, 0.7345679012345679, 0.7469135802469136, 0.7763975155279503], 'avgPrecision': 0.7614523426117629, 'recall': [0.8024691358024691, 0.7469135802469136, 0.7345679012345679, 0.7469135802469136, 0.7763975155279503], 'avgRecall': 0.7614523426117629, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T00:32:49.909952 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.6975308641975309, 0.7469135802469136, 0.7283950617283951, 0.7716049382716049, 0.6770186335403726], 'avgAccuracy': 0.7242926155969635, 'f1': [0.6916120799161735, 0.7398052194345642, 0.7374189541376809, 0.7709273383876558, 0.6898458462345624], 'avgF1': 0.7259218876221274, 'precision': [0.6975308641975309, 0.7469135802469136, 0.7283950617283951, 0.7716049382716049, 0.6770186335403726], 'avgPrecision': 0.7242926155969635, 'recall': [0.6975308641975309, 0.7469135802469136, 0.7283950617283951, 0.7716049382716049, 0.6770186335403726], 'avgRecall': 0.7242926155969635, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T00:32:51.558886 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.6851851851851852, 0.7222222222222222, 0.7160493827160493, 0.8024691358024691, 0.7453416149068323], 'avgAccuracy': 0.7342535081665517, 'f1': [0.6247762365811498, 0.6521838276834049, 0.6587090548281654, 0.7615457491672244, 0.6973026947598235], 'avgF1': 0.6789035126039537, 'precision': [0.6851851851851852, 0.7222222222222222, 0.7160493827160493, 0.8024691358024691, 0.7453416149068323], 'avgPrecision': 0.7342535081665517, 'recall': [0.6851851851851852, 0.7222222222222222, 0.7160493827160493, 0.8024691358024691, 0.7453416149068323], 'avgRecall': 0.7342535081665517, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T00:36:10.959845 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.7222222222222222, 0.7283950617283951, 0.7345679012345679, 0.808641975308642, 0.7701863354037267], 'avgAccuracy': 0.7528026991795108, 'f1': [0.694284684440048, 0.6920673587340254, 0.708425791441405, 0.7871372086518036, 0.7538686880389269], 'avgF1': 0.7271567462612418, 'precision': [0.7222222222222222, 0.7283950617283951, 0.7345679012345679, 0.808641975308642, 0.7701863354037267], 'avgPrecision': 0.7528026991795108, 'recall': [0.7222222222222222, 0.7283950617283951, 0.7345679012345679, 0.808641975308642, 0.7701863354037267], 'avgRecall': 0.7528026991795108, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.7777777777777778, 0.7654320987654321, 0.7839506172839507, 0.808641975308642, 0.782608695652174], 'avgAccuracy': 0.7836822329575953, 'f1': [0.7649507429518596, 0.7489167991388527, 0.7811684126791516, 0.7980946596423862, 0.780555700541086], 'avgF1': 0.7747372629906673, 'precision': [0.7777777777777778, 0.7654320987654321, 0.7839506172839507, 0.808641975308642, 0.782608695652174], 'avgPrecision': 0.7836822329575953, 'recall': [0.7777777777777778, 0.7654320987654321, 0.7839506172839507, 0.808641975308642, 0.782608695652174], 'avgRecall': 0.7836822329575953, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.781205  0.770564   0.781205  0.781205   
1  0.739169  0.716012   0.739169  0.739169   
2  0.745388  0.714298   0.745388  0.745388   
3  0.627935  0.597992   0.627935  0.627935   
4  0.693474  0.695955   0.693474  0.693474   
5  0.663768  0.668657   0.663768  0.663768   
6  0.734254  0.678904   0.734254  0.734254   
7  0.752803  0.727157   0.752803  0.752803   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T00:43:07.818502 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.7716049382716049, 0.7777777777777778, 0.7777777777777778, 0.808641975308642, 0.7950310559006211], 'avgAccuracy': 0.7861667050072847, 'f1': [0.7595211372989151, 0.7626418545481423, 0.7768943603186974, 0.8007809027923971, 0.7893322175356852], 'avgF1': 0.7778340944987674, 'precision': [0.7716049382716049, 0.7777777777777778, 0.7777777777777778, 0.808641975308642, 0.7950310559006211], 'avgPrecision': 0.7861667050072847, 'recall': [0.7716049382716049, 0.7777777777777778, 0.7777777777777778, 0.808641975308642, 0.7950310559006211], 'avgRecall': 0.7861667050072847, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T00:43:14.067470 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.7222222222222222, 0.7098765432098766, 0.7530864197530864, 0.8271604938271605, 0.7267080745341615], 'avgAccuracy': 0.7478107507093015, 'f1': [0.6938065765651973, 0.6873991112668157, 0.7321643933933605, 0.8088803361845122, 0.715383058287078], 'avgF1': 0.7275266951393927, 'precision': [0.7222222222222222, 0.7098765432098766, 0.7530864197530864, 0.8271604938271605, 0.7267080745341615], 'avgPrecision': 0.7478107507093015, 'recall': [0.7222222222222222, 0.7098765432098766, 0.7530864197530864, 0.8271604938271605, 0.7267080745341615], 'avgRecall': 0.7478107507093015, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T00:43:28.031914 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.7222222222222222, 0.7098765432098766, 0.7407407407407407, 0.7839506172839507, 0.7763975155279503], 'avgAccuracy': 0.7466375277969481, 'f1': [0.6908352445550514, 0.6752552467072781, 0.7036521859071018, 0.7529662742442822, 0.7545485473225829], 'avgF1': 0.7154514997472593, 'precision': [0.7222222222222222, 0.7098765432098766, 0.7407407407407407, 0.7839506172839507, 0.7763975155279503], 'avgPrecision': 0.7466375277969481, 'recall': [0.7222222222222222, 0.7098765432098766, 0.7407407407407407, 0.7839506172839507, 0.7763975155279503], 'avgRecall': 0.7466375277969481, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T00:43:28.418424 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.5679012345679012, 0.6234567901234568, 0.6358024691358025, 0.6790123456790124, 0.6335403726708074], 'avgAccuracy': 0.6279426424353961, 'f1': [0.5366536949203194, 0.5766598710310114, 0.5997358392925316, 0.6616356136219576, 0.6143264060876589], 'avgF1': 0.5978022849906958, 'precision': [0.5679012345679012, 0.6234567901234568, 0.6358024691358025, 0.6790123456790124, 0.6335403726708074], 'avgPrecision': 0.6279426424353961, 'recall': [0.5679012345679012, 0.6234567901234568, 0.6358024691358025, 0.6790123456790124, 0.6335403726708074], 'avgRecall': 0.6279426424353961, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T00:43:34.055366 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.8024691358024691, 0.7469135802469136, 0.7160493827160493, 0.7716049382716049, 0.8198757763975155], 'avgAccuracy': 0.7713825626869105, 'f1': [0.7850273844690371, 0.7130611035002123, 0.7007261262927997, 0.716189340281835, 0.8110195581108012], 'avgF1': 0.7452047025309371, 'precision': [0.8024691358024691, 0.7469135802469136, 0.7160493827160493, 0.7716049382716049, 0.8198757763975155], 'avgPrecision': 0.7713825626869105, 'recall': [0.8024691358024691, 0.7469135802469136, 0.7160493827160493, 0.7716049382716049, 0.8198757763975155], 'avgRecall': 0.7713825626869105, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-29T00:43:34.895141 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.6790123456790124, 0.7530864197530864, 0.7160493827160493, 0.7530864197530864, 0.7080745341614907], 'avgAccuracy': 0.7218618204125451, 'f1': [0.6767695822591124, 0.7516529358234076, 0.7187461642760293, 0.7542241433610481, 0.7231346345702058], 'avgF1': 0.7249054920579606, 'precision': [0.6790123456790124, 0.7530864197530864, 0.7160493827160493, 0.7530864197530864, 0.7080745341614907], 'avgPrecision': 0.7218618204125451, 'recall': [0.6790123456790124, 0.7530864197530864, 0.7160493827160493, 0.7530864197530864, 0.7080745341614907], 'avgRecall': 0.7218618204125451, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-29T00:43:36.168008 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.691358024691358, 0.7222222222222222, 0.7160493827160493, 0.8024691358024691, 0.7453416149068323], 'avgAccuracy': 0.7354880760677862, 'f1': [0.6299816852700939, 0.6521838276834049, 0.6587090548281654, 0.7615781646724789, 0.6973026947598235], 'avgF1': 0.6799510854427934, 'precision': [0.691358024691358, 0.7222222222222222, 0.7160493827160493, 0.8024691358024691, 0.7453416149068323], 'avgPrecision': 0.7354880760677862, 'recall': [0.691358024691358, 0.7222222222222222, 0.7160493827160493, 0.8024691358024691, 0.7453416149068323], 'avgRecall': 0.7354880760677862, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-29T00:46:52.559130 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.7160493827160493, 0.7345679012345679, 0.7407407407407407, 0.8148148148148148, 0.7763975155279503], 'avgAccuracy': 0.7565140710068247, 'f1': [0.6891717631382676, 0.705461852045055, 0.7098126756358286, 0.7922402384126432, 0.7584608860261174], 'avgF1': 0.7310294830515823, 'precision': [0.7160493827160493, 0.7345679012345679, 0.7407407407407407, 0.8148148148148148, 0.7763975155279503], 'avgPrecision': 0.7565140710068247, 'recall': [0.7160493827160493, 0.7345679012345679, 0.7407407407407407, 0.8148148148148148, 0.7763975155279503], 'avgRecall': 0.7565140710068247, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.7716049382716049, 0.7777777777777778, 0.7777777777777778, 0.808641975308642, 0.7950310559006211], 'avgAccuracy': 0.7861667050072847, 'f1': [0.7595211372989151, 0.7626418545481423, 0.7768943603186974, 0.8007809027923971, 0.7893322175356852], 'avgF1': 0.7778340944987674, 'precision': [0.7716049382716049, 0.7777777777777778, 0.7777777777777778, 0.808641975308642, 0.7950310559006211], 'avgPrecision': 0.7861667050072847, 'recall': [0.7716049382716049, 0.7777777777777778, 0.7777777777777778, 0.808641975308642, 0.7950310559006211], 'avgRecall': 0.7861667050072847, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.772548  0.761975   0.772548  0.772548   
1  0.739169  0.716012   0.739169  0.739169   
2  0.746638  0.715451   0.746638  0.746638   
3  0.627943  0.597802   0.627943  0.627943   
4  0.694709  0.698175   0.694709  0.694709   
5  0.689748  0.692638   0.689748  0.689748   
6  0.735488  0.679951   0.735488  0.735488   
7  0.752795  0.723470   0.752795  0.752795   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T00:53:41.118491 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.7777777777777778, 0.7716049382716049, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgAccuracy': 0.8084042634767272, 'f1': [0.759968034439952, 0.7593727305737109, 0.8190718272222394, 0.8441002569551879, 0.7981547453504438], 'avgF1': 0.7961335189083067, 'precision': [0.7777777777777778, 0.7716049382716049, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgPrecision': 0.8084042634767272, 'recall': [0.7777777777777778, 0.7716049382716049, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgRecall': 0.8084042634767272, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T00:53:47.560336 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.7283950617283951, 0.7345679012345679, 0.8024691358024691, 0.8209876543209876, 0.7515527950310559], 'avgAccuracy': 0.7675945096234952, 'f1': [0.7041086071930184, 0.7202436099081435, 0.7811603071650352, 0.7939204179041133, 0.7310526657982823], 'avgF1': 0.7460971215937185, 'precision': [0.7283950617283951, 0.7345679012345679, 0.8024691358024691, 0.8209876543209876, 0.7515527950310559], 'avgPrecision': 0.7675945096234952, 'recall': [0.7283950617283951, 0.7345679012345679, 0.8024691358024691, 0.8209876543209876, 0.7515527950310559], 'avgRecall': 0.7675945096234952, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T00:54:01.561941 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.7222222222222222, 0.7098765432098766, 0.7345679012345679, 0.7901234567901234, 0.7391304347826086], 'avgAccuracy': 0.7391841116478798, 'f1': [0.690213994944468, 0.6761962687888614, 0.6990770304148337, 0.7618666474857406, 0.7242236024844722], 'avgF1': 0.7103155088236752, 'precision': [0.7222222222222222, 0.7098765432098766, 0.7345679012345679, 0.7901234567901234, 0.7391304347826086], 'avgPrecision': 0.7391841116478798, 'recall': [0.7222222222222222, 0.7098765432098766, 0.7345679012345679, 0.7901234567901234, 0.7391304347826086], 'avgRecall': 0.7391841116478798, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T00:54:01.946493 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.5679012345679012, 0.6172839506172839, 0.6419753086419753, 0.6790123456790124, 0.6335403726708074], 'avgAccuracy': 0.627942642435396, 'f1': [0.5366536949203194, 0.5715527715849699, 0.6046651920729372, 0.6616356136219576, 0.6143264060876589], 'avgF1': 0.5977667356575685, 'precision': [0.5679012345679012, 0.6172839506172839, 0.6419753086419753, 0.6790123456790124, 0.6335403726708074], 'avgPrecision': 0.627942642435396, 'recall': [0.5679012345679012, 0.6172839506172839, 0.6419753086419753, 0.6790123456790124, 0.6335403726708074], 'avgRecall': 0.627942642435396, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T00:54:07.740892 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.8024691358024691, 0.782608695652174], 'avgAccuracy': 0.7886205045625335, 'f1': [0.7959756180051349, 0.7622186081579934, 0.7446938357640677, 0.7788172945347881, 0.7764652687766721], 'avgF1': 0.7716341250477312, 'precision': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.8024691358024691, 0.782608695652174], 'avgPrecision': 0.7886205045625335, 'recall': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.8024691358024691, 0.782608695652174], 'avgRecall': 0.7886205045625335, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T00:54:08.555182 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.7407407407407407, 0.7407407407407407, 0.7777777777777778, 0.8024691358024691, 0.7453416149068323], 'avgAccuracy': 0.7614140019937121, 'f1': [0.7324852344460188, 0.7395956342816247, 0.7803360853589164, 0.7855454774502394, 0.7586097149024201], 'avgF1': 0.7593144292878439, 'precision': [0.7407407407407407, 0.7407407407407407, 0.7777777777777778, 0.8024691358024691, 0.7453416149068323], 'avgPrecision': 0.7614140019937121, 'recall': [0.7407407407407407, 0.7407407407407407, 0.7777777777777778, 0.8024691358024691, 0.7453416149068323], 'avgRecall': 0.7614140019937121, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T00:54:09.857061 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.691358024691358, 0.7222222222222222, 0.7222222222222222, 0.8024691358024691, 0.7453416149068323], 'avgAccuracy': 0.7367226439690208, 'f1': [0.6299816852700939, 0.6521838276834049, 0.6626209092866769, 0.7615457491672244, 0.6973026947598235], 'avgF1': 0.6807269732334448, 'precision': [0.691358024691358, 0.7222222222222222, 0.7222222222222222, 0.8024691358024691, 0.7453416149068323], 'avgPrecision': 0.7367226439690208, 'recall': [0.691358024691358, 0.7222222222222222, 0.7222222222222222, 0.8024691358024691, 0.7453416149068323], 'avgRecall': 0.7367226439690208, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-29T00:57:23.929041 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.7222222222222222, 0.7222222222222222, 0.7283950617283951, 0.7839506172839507, 0.7515527950310559], 'avgAccuracy': 0.7416685836975692, 'f1': [0.694284684440048, 0.6752871445242922, 0.6990248948582282, 0.757942947464176, 0.7414181182462946], 'avgF1': 0.7135915579066078, 'precision': [0.7222222222222222, 0.7222222222222222, 0.7283950617283951, 0.7839506172839507, 0.7515527950310559], 'avgPrecision': 0.7416685836975692, 'recall': [0.7222222222222222, 0.7222222222222222, 0.7283950617283951, 0.7839506172839507, 0.7515527950310559], 'avgRecall': 0.7416685836975692, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.7777777777777778, 0.7716049382716049, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgAccuracy': 0.8084042634767272, 'f1': [0.759968034439952, 0.7593727305737109, 0.8190718272222394, 0.8441002569551879, 0.7981547453504438], 'avgF1': 0.7961335189083067, 'precision': [0.7777777777777778, 0.7716049382716049, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgPrecision': 0.8084042634767272, 'recall': [0.7777777777777778, 0.7716049382716049, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgRecall': 0.8084042634767272, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.800989  0.787600   0.800989  0.800989   
1  0.763898  0.738965   0.763898  0.763898   
2  0.739184  0.710316   0.739184  0.739184   
3  0.627943  0.597767   0.627943  0.627943   
4  0.695898  0.702459   0.695898  0.695898   
5  0.729231  0.724490   0.729231  0.729231   
6  0.736723  0.680727   0.736723  0.736723   
7  0.741669  0.713592   0.741669  0.741669   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T01:04:22.297590 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.7901234567901234, 0.7654320987654321, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgAccuracy': 0.8096388313779618, 'f1': [0.7764324153213044, 0.7512510793353355, 0.8204987983083804, 0.84118037811971, 0.8019796149328074], 'avgF1': 0.7982684572035076, 'precision': [0.7901234567901234, 0.7654320987654321, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgPrecision': 0.8096388313779618, 'recall': [0.7901234567901234, 0.7654320987654321, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgRecall': 0.8096388313779618, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T01:04:28.633722 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.7160493827160493, 0.7407407407407407, 0.8271604938271605, 0.8209876543209876, 0.7639751552795031], 'avgAccuracy': 0.7737826853768882, 'f1': [0.6942618424099904, 0.7259982405916384, 0.8114090124983371, 0.8019494270074943, 0.7514211793128939], 'avgF1': 0.7570079403640708, 'precision': [0.7160493827160493, 0.7407407407407407, 0.8271604938271605, 0.8209876543209876, 0.7639751552795031], 'avgPrecision': 0.7737826853768882, 'recall': [0.7160493827160493, 0.7407407407407407, 0.8271604938271605, 0.8209876543209876, 0.7639751552795031], 'avgRecall': 0.7737826853768882, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 13, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T01:04:41.999206 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.7222222222222222, 0.7160493827160493, 0.7345679012345679, 0.7901234567901234, 0.7391304347826086], 'avgAccuracy': 0.7404186795491143, 'f1': [0.690213994944468, 0.6763124263291767, 0.6990770304148337, 0.7608955516362923, 0.7242236024844722], 'avgF1': 0.7101445211618486, 'precision': [0.7222222222222222, 0.7160493827160493, 0.7345679012345679, 0.7901234567901234, 0.7391304347826086], 'avgPrecision': 0.7404186795491143, 'recall': [0.7222222222222222, 0.7160493827160493, 0.7345679012345679, 0.7901234567901234, 0.7391304347826086], 'avgRecall': 0.7404186795491143, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T01:04:42.480760 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.5740740740740741, 0.6358024691358025, 0.6419753086419753, 0.6975308641975309, 0.6459627329192547], 'avgAccuracy': 0.6390690897937275, 'f1': [0.5418618394008063, 0.5893428636775743, 0.6041723822588019, 0.6766916899261395, 0.6242426163011788], 'avgF1': 0.6072622783129001, 'precision': [0.5740740740740741, 0.6358024691358025, 0.6419753086419753, 0.6975308641975309, 0.6459627329192547], 'avgPrecision': 0.6390690897937275, 'recall': [0.5740740740740741, 0.6358024691358025, 0.6419753086419753, 0.6975308641975309, 0.6459627329192547], 'avgRecall': 0.6390690897937275, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T01:04:48.145136 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.8024691358024691, 0.782608695652174], 'avgAccuracy': 0.7886205045625335, 'f1': [0.7959756180051349, 0.7622186081579934, 0.7446938357640677, 0.7788172945347881, 0.7764652687766721], 'avgF1': 0.7716341250477312, 'precision': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.8024691358024691, 0.782608695652174], 'avgPrecision': 0.7886205045625335, 'recall': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.8024691358024691, 0.782608695652174], 'avgRecall': 0.7886205045625335, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T01:04:48.957733 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.7098765432098766, 0.7592592592592593, 0.7901234567901234, 0.8333333333333334, 0.7577639751552795], 'avgAccuracy': 0.7700713135495745, 'f1': [0.7033231484922305, 0.7539464930769278, 0.788709508226995, 0.8249202169948936, 0.7571211511319227], 'avgF1': 0.765604103584594, 'precision': [0.7098765432098766, 0.7592592592592593, 0.7901234567901234, 0.8333333333333334, 0.7577639751552795], 'avgPrecision': 0.7700713135495745, 'recall': [0.7098765432098766, 0.7592592592592593, 0.7901234567901234, 0.8333333333333334, 0.7577639751552795], 'avgRecall': 0.7700713135495745, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T01:04:50.391515 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.691358024691358, 0.7283950617283951, 0.7222222222222222, 0.8024691358024691, 0.7453416149068323], 'avgAccuracy': 0.7379572118702553, 'f1': [0.6299816852700939, 0.6565014487252122, 0.6626209092866769, 0.7615457491672244, 0.6973026947598235], 'avgF1': 0.6815904974418062, 'precision': [0.691358024691358, 0.7283950617283951, 0.7222222222222222, 0.8024691358024691, 0.7453416149068323], 'avgPrecision': 0.7379572118702553, 'recall': [0.691358024691358, 0.7283950617283951, 0.7222222222222222, 0.8024691358024691, 0.7453416149068323], 'avgRecall': 0.7379572118702553, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T01:08:32.739057 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.7098765432098766, 0.7222222222222222, 0.7469135802469136, 0.8024691358024691, 0.7515527950310559], 'avgAccuracy': 0.7466068553025075, 'f1': [0.6751393215606801, 0.6743677334292222, 0.7161324875111961, 0.7726414265437769, 0.732825634201855], 'avgF1': 0.7142213206493461, 'precision': [0.7098765432098766, 0.7222222222222222, 0.7469135802469136, 0.8024691358024691, 0.7515527950310559], 'avgPrecision': 0.7466068553025075, 'recall': [0.7098765432098766, 0.7222222222222222, 0.7469135802469136, 0.8024691358024691, 0.7515527950310559], 'avgRecall': 0.7466068553025075, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.7901234567901234, 0.7654320987654321, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgAccuracy': 0.8096388313779618, 'f1': [0.7764324153213044, 0.7512510793353355, 0.8204987983083804, 0.84118037811971, 0.8019796149328074], 'avgF1': 0.7982684572035076, 'precision': [0.7901234567901234, 0.7654320987654321, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgPrecision': 0.8096388313779618, 'recall': [0.7901234567901234, 0.7654320987654321, 0.8271604938271605, 0.8580246913580247, 0.8074534161490683], 'avgRecall': 0.8096388313779618, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.804693  0.793092   0.804693  0.804693   
1  0.767595  0.743996   0.767595  0.767595   
2  0.740419  0.710145   0.740419  0.740419   
3  0.639069  0.607262   0.639069  0.639069   
4  0.695875  0.702569   0.695875  0.695875   
5  0.736661  0.733491   0.736661  0.736661   
6  0.737957  0.681590   0.737957  0.737957   
7  0.737957  0.708151   0.737957  0.737957   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T01:16:58.690439 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.7839506172839507, 0.7716049382716049, 0.8395061728395061, 0.8518518518518519, 0.8074534161490683], 'avgAccuracy': 0.8108733992791963, 'f1': [0.7708598897602335, 0.7593727305737109, 0.831393578191329, 0.8356719646669222, 0.8019796149328074], 'avgF1': 0.7998555556250005, 'precision': [0.7839506172839507, 0.7716049382716049, 0.8395061728395061, 0.8518518518518519, 0.8074534161490683], 'avgPrecision': 0.8108733992791963, 'recall': [0.7839506172839507, 0.7716049382716049, 0.8395061728395061, 0.8518518518518519, 0.8074534161490683], 'avgRecall': 0.8108733992791963, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T01:17:05.693061 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.7160493827160493, 0.7469135802469136, 0.8271604938271605, 0.8271604938271605, 0.7639751552795031], 'avgAccuracy': 0.7762518211793574, 'f1': [0.6942618424099904, 0.7341678715858283, 0.8114090124983371, 0.8106087594522268, 0.7514211793128939], 'avgF1': 0.7603737330518553, 'precision': [0.7160493827160493, 0.7469135802469136, 0.8271604938271605, 0.8271604938271605, 0.7639751552795031], 'avgPrecision': 0.7762518211793574, 'recall': [0.7160493827160493, 0.7469135802469136, 0.8271604938271605, 0.8271604938271605, 0.7639751552795031], 'avgRecall': 0.7762518211793574, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 13, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T01:17:21.464812 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.7222222222222222, 0.7160493827160493, 0.7469135802469136, 0.7962962962962963, 0.7391304347826086], 'avgAccuracy': 0.744122383252818, 'f1': [0.690213994944468, 0.6763124263291767, 0.7082517657853898, 0.766812523521648, 0.7242236024844722], 'avgF1': 0.713162862613031, 'precision': [0.7222222222222222, 0.7160493827160493, 0.7469135802469136, 0.7962962962962963, 0.7391304347826086], 'avgPrecision': 0.744122383252818, 'recall': [0.7222222222222222, 0.7160493827160493, 0.7469135802469136, 0.7962962962962963, 0.7391304347826086], 'avgRecall': 0.744122383252818, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T01:17:21.964813 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.6049382716049383, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6583850931677019], 'avgAccuracy': 0.6514301050532935, 'f1': [0.5649573882318354, 0.5943597040274312, 0.614114724480578, 0.6754538991063272, 0.6340657422484931], 'avgF1': 0.616590291618933, 'precision': [0.6049382716049383, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6583850931677019], 'avgPrecision': 0.6514301050532935, 'recall': [0.6049382716049383, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6583850931677019], 'avgRecall': 0.6514301050532935, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T01:17:28.978372 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.8024691358024691, 0.782608695652174], 'avgAccuracy': 0.7886205045625335, 'f1': [0.7959756180051349, 0.7622186081579934, 0.7446938357640677, 0.7788172945347881, 0.7764652687766721], 'avgF1': 0.7716341250477312, 'precision': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.8024691358024691, 0.782608695652174], 'avgPrecision': 0.7886205045625335, 'recall': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.8024691358024691, 0.782608695652174], 'avgRecall': 0.7886205045625335, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T01:17:29.944791 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.7098765432098766, 0.7407407407407407, 0.808641975308642, 0.8024691358024691, 0.7763975155279503], 'avgAccuracy': 0.7676251821179357, 'f1': [0.6978515311848644, 0.7397376730629099, 0.8100594063237742, 0.7865227016528813, 0.7839559964154383], 'avgF1': 0.7636254617279736, 'precision': [0.7098765432098766, 0.7407407407407407, 0.808641975308642, 0.8024691358024691, 0.7763975155279503], 'avgPrecision': 0.7676251821179357, 'recall': [0.7098765432098766, 0.7407407407407407, 0.808641975308642, 0.8024691358024691, 0.7763975155279503], 'avgRecall': 0.7676251821179357, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T01:17:31.816345 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.691358024691358, 0.7283950617283951, 0.7283950617283951, 0.8024691358024691, 0.7453416149068323], 'avgAccuracy': 0.7391917797714899, 'f1': [0.6299816852700939, 0.6565014487252122, 0.6665423930921565, 0.7615457491672244, 0.6973026947598235], 'avgF1': 0.6823747942029021, 'precision': [0.691358024691358, 0.7283950617283951, 0.7283950617283951, 0.8024691358024691, 0.7453416149068323], 'avgPrecision': 0.7391917797714899, 'recall': [0.691358024691358, 0.7283950617283951, 0.7283950617283951, 0.8024691358024691, 0.7453416149068323], 'avgRecall': 0.7391917797714899, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T01:21:01.955118 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.7160493827160493, 0.7222222222222222, 0.7530864197530864, 0.7839506172839507, 0.7639751552795031], 'avgAccuracy': 0.7478567594509623, 'f1': [0.6848298191581773, 0.6752871445242922, 0.7188373970549606, 0.7589270339037866, 0.7436255165103157], 'avgF1': 0.7163013822303065, 'precision': [0.7160493827160493, 0.7222222222222222, 0.7530864197530864, 0.7839506172839507, 0.7639751552795031], 'avgPrecision': 0.7478567594509623, 'recall': [0.7160493827160493, 0.7222222222222222, 0.7530864197530864, 0.7839506172839507, 0.7639751552795031], 'avgRecall': 0.7478567594509623, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.7839506172839507, 0.7716049382716049, 0.8395061728395061, 0.8518518518518519, 0.8074534161490683], 'avgAccuracy': 0.8108733992791963, 'f1': [0.7708598897602335, 0.7593727305737109, 0.831393578191329, 0.8356719646669222, 0.8019796149328074], 'avgF1': 0.7998555556250005, 'precision': [0.7839506172839507, 0.7716049382716049, 0.8395061728395061, 0.8518518518518519, 0.8074534161490683], 'avgPrecision': 0.8108733992791963, 'recall': [0.7839506172839507, 0.7716049382716049, 0.8395061728395061, 0.8518518518518519, 0.8074534161490683], 'avgRecall': 0.8108733992791963, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.807162  0.795751   0.807162  0.807162   
1  0.766360  0.742331   0.766360  0.766360   
2  0.744122  0.713163   0.744122  0.744122   
3  0.651430  0.616590   0.651430  0.651430   
4  0.695844  0.702450   0.695844  0.695844   
5  0.747795  0.743047   0.747795  0.747795   
6  0.739192  0.682375   0.739192  0.739192   
7  0.741669  0.711985   0.741669  0.741669   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T01:28:40.026475 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.7901234567901234, 0.7777777777777778, 0.8333333333333334, 0.8580246913580247, 0.8012422360248447], 'avgAccuracy': 0.8121002990568208, 'f1': [0.7764324153213044, 0.7673490995121218, 0.8244555416840063, 0.8441002569551879, 0.7967539172751386], 'avgF1': 0.8018182461495518, 'precision': [0.7901234567901234, 0.7777777777777778, 0.8333333333333334, 0.8580246913580247, 0.8012422360248447], 'avgPrecision': 0.8121002990568208, 'recall': [0.7901234567901234, 0.7777777777777778, 0.8333333333333334, 0.8580246913580247, 0.8012422360248447], 'avgRecall': 0.8121002990568208, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T01:28:47.299897 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.7160493827160493, 0.7469135802469136, 0.8333333333333334, 0.8148148148148148, 0.7639751552795031], 'avgAccuracy': 0.7750172532781229, 'f1': [0.6942618424099904, 0.7341678715858283, 0.819800707168113, 0.7930492239534903, 0.7527242644203018], 'avgF1': 0.7588007819075447, 'precision': [0.7160493827160493, 0.7469135802469136, 0.8333333333333334, 0.8148148148148148, 0.7639751552795031], 'avgPrecision': 0.7750172532781229, 'recall': [0.7160493827160493, 0.7469135802469136, 0.8333333333333334, 0.8148148148148148, 0.7639751552795031], 'avgRecall': 0.7750172532781229, 'params': [{'algorithm': 'kd_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 13, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T01:29:01.468762 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.7222222222222222, 0.7160493827160493, 0.7469135802469136, 0.8024691358024691, 0.7391304347826086], 'avgAccuracy': 0.7453569511540525, 'f1': [0.690213994944468, 0.6763124263291767, 0.7082517657853898, 0.7718101397872715, 0.7242236024844722], 'avgF1': 0.7141623858661557, 'precision': [0.7222222222222222, 0.7160493827160493, 0.7469135802469136, 0.8024691358024691, 0.7391304347826086], 'avgPrecision': 0.7453569511540525, 'recall': [0.7222222222222222, 0.7160493827160493, 0.7469135802469136, 0.8024691358024691, 0.7391304347826086], 'avgRecall': 0.7453569511540525, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T01:29:01.921837 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.5802469135802469, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6583850931677019], 'avgAccuracy': 0.6464918334483551, 'f1': [0.5457079001989656, 0.5943597040274312, 0.614114724480578, 0.6754538991063272, 0.6340657422484931], 'avgF1': 0.612740394012359, 'precision': [0.5802469135802469, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6583850931677019], 'avgPrecision': 0.6464918334483551, 'recall': [0.5802469135802469, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6583850931677019], 'avgRecall': 0.6464918334483551, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T01:29:07.442261 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.808641975308642, 0.7763975155279503], 'avgAccuracy': 0.7886128364389234, 'f1': [0.7959756180051349, 0.7622186081579934, 0.7422356995504344, 0.7791080293609681, 0.775322224254452], 'avgF1': 0.7709720358657965, 'precision': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.808641975308642, 0.7763975155279503], 'avgPrecision': 0.7886128364389234, 'recall': [0.808641975308642, 0.7901234567901234, 0.7592592592592593, 0.808641975308642, 0.7763975155279503], 'avgRecall': 0.7886128364389234, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T01:29:08.306698 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.7530864197530864, 0.808641975308642, 0.8148148148148148, 0.8024691358024691, 0.7267080745341615], 'avgAccuracy': 0.7811440840426348, 'f1': [0.7507963011150625, 0.8069308059598861, 0.8103465612593403, 0.7927838051294842, 0.7299511808219027], 'avgF1': 0.7781617308571351, 'precision': [0.7530864197530864, 0.808641975308642, 0.8148148148148148, 0.8024691358024691, 0.7267080745341615], 'avgPrecision': 0.7811440840426348, 'recall': [0.7530864197530864, 0.808641975308642, 0.8148148148148148, 0.8024691358024691, 0.7267080745341615], 'avgRecall': 0.7811440840426348, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T01:29:09.726351 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.691358024691358, 0.7222222222222222, 0.7283950617283951, 0.8024691358024691, 0.7453416149068323], 'avgAccuracy': 0.7379572118702553, 'f1': [0.6299816852700939, 0.6521838276834049, 0.6665423930921565, 0.7615457491672244, 0.6973026947598235], 'avgF1': 0.6815112699945406, 'precision': [0.691358024691358, 0.7222222222222222, 0.7283950617283951, 0.8024691358024691, 0.7453416149068323], 'avgPrecision': 0.7379572118702553, 'recall': [0.691358024691358, 0.7222222222222222, 0.7283950617283951, 0.8024691358024691, 0.7453416149068323], 'avgRecall': 0.7379572118702553, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T01:32:23.673381 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.7222222222222222, 0.7222222222222222, 0.7407407407407407, 0.808641975308642, 0.7453416149068323], 'avgAccuracy': 0.7478337550801318, 'f1': [0.694284684440048, 0.6752871445242922, 0.7078161504218767, 0.7776319818886617, 0.7355843859923952], 'avgF1': 0.7181208694534548, 'precision': [0.7222222222222222, 0.7222222222222222, 0.7407407407407407, 0.808641975308642, 0.7453416149068323], 'avgPrecision': 0.7478337550801318, 'recall': [0.7222222222222222, 0.7222222222222222, 0.7407407407407407, 0.808641975308642, 0.7453416149068323], 'avgRecall': 0.7478337550801318, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.7901234567901234, 0.7777777777777778, 0.8333333333333334, 0.8580246913580247, 0.8012422360248447], 'avgAccuracy': 0.8121002990568208, 'f1': [0.7764324153213044, 0.7673490995121218, 0.8244555416840063, 0.8441002569551879, 0.7967539172751386], 'avgF1': 0.8018182461495518, 'precision': [0.7901234567901234, 0.7777777777777778, 0.8333333333333334, 0.8580246913580247, 0.8012422360248447], 'avgPrecision': 0.8121002990568208, 'recall': [0.7901234567901234, 0.7777777777777778, 0.8333333333333334, 0.8580246913580247, 0.8012422360248447], 'avgRecall': 0.8121002990568208, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.807162  0.795121   0.807162  0.807162   
1  0.770064  0.747968   0.770064  0.770064   
2  0.745357  0.714162   0.745357  0.745357   
3  0.646492  0.612740   0.646492  0.646492   
4  0.688436  0.694065   0.688436  0.688436   
5  0.735450  0.729809   0.735450  0.735450   
6  0.737957  0.681511   0.737957  0.737957   
7  0.739184  0.712249   0.739184  0.739184   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T01:39:11.946657 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.7777777777777778, 0.7716049382716049, 0.8333333333333334, 0.8641975308641975, 0.8074534161490683], 'avgAccuracy': 0.8108733992791963, 'f1': [0.7656776094276094, 0.756524053413012, 0.8221770092330599, 0.8496851086689412, 0.8019796149328074], 'avgF1': 0.799208679135086, 'precision': [0.7777777777777778, 0.7716049382716049, 0.8333333333333334, 0.8641975308641975, 0.8074534161490683], 'avgPrecision': 0.8108733992791963, 'recall': [0.7777777777777778, 0.7716049382716049, 0.8333333333333334, 0.8641975308641975, 0.8074534161490683], 'avgRecall': 0.8108733992791963, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T01:39:18.309204 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.7345679012345679, 0.7283950617283951, 0.8209876543209876, 0.8148148148148148, 0.7639751552795031], 'avgAccuracy': 0.7725481174756537, 'f1': [0.7101654063402697, 0.7091745347226361, 0.8061490978157645, 0.7930492239534903, 0.7527242644203018], 'avgF1': 0.7542525054504925, 'precision': [0.7345679012345679, 0.7283950617283951, 0.8209876543209876, 0.8148148148148148, 0.7639751552795031], 'avgPrecision': 0.7725481174756537, 'recall': [0.7345679012345679, 0.7283950617283951, 0.8209876543209876, 0.8148148148148148, 0.7639751552795031], 'avgRecall': 0.7725481174756537, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T01:39:32.306417 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.7222222222222222, 0.7037037037037037, 0.7530864197530864, 0.7962962962962963, 0.7391304347826086], 'avgAccuracy': 0.7428878153515834, 'f1': [0.690213994944468, 0.6708837693609266, 0.7141149609494385, 0.765889676479118, 0.7242236024844722], 'avgF1': 0.7130652008436846, 'precision': [0.7222222222222222, 0.7037037037037037, 0.7530864197530864, 0.7962962962962963, 0.7391304347826086], 'avgPrecision': 0.7428878153515834, 'recall': [0.7222222222222222, 0.7037037037037037, 0.7530864197530864, 0.7962962962962963, 0.7391304347826086], 'avgRecall': 0.7428878153515834, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T01:39:32.712622 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.5925925925925926, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6459627329192547], 'avgAccuracy': 0.6464764972011349, 'f1': [0.5573149900812726, 0.5943597040274312, 0.6152045175846284, 0.6754538991063272, 0.6239731265983129], 'avgF1': 0.6132612474795944, 'precision': [0.5925925925925926, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6459627329192547], 'avgPrecision': 0.6464764972011349, 'recall': [0.5925925925925926, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6459627329192547], 'avgRecall': 0.6464764972011349, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T01:39:38.605195 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.8148148148148148, 0.8580246913580247, 0.808641975308642, 0.8395061728395061, 0.7391304347826086], 'avgAccuracy': 0.8120236178207193, 'f1': [0.8041864514086737, 0.8513811142635619, 0.8007207825796381, 0.8264848601798486, 0.7477536156941519], 'avgF1': 0.8061053648251748, 'precision': [0.8148148148148148, 0.8580246913580247, 0.808641975308642, 0.8395061728395061, 0.7391304347826086], 'avgPrecision': 0.8120236178207193, 'recall': [0.8148148148148148, 0.8580246913580247, 0.808641975308642, 0.8395061728395061, 0.7391304347826086], 'avgRecall': 0.8120236178207193, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T01:39:39.421470 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.7345679012345679, 0.7345679012345679, 0.8271604938271605, 0.8271604938271605, 0.7763975155279503], 'avgAccuracy': 0.7799708611302815, 'f1': [0.7279073945740612, 0.7284065639138103, 0.8217107772813222, 0.8038057588357322, 0.7801992417520367], 'avgF1': 0.7724059472713926, 'precision': [0.7345679012345679, 0.7345679012345679, 0.8271604938271605, 0.8271604938271605, 0.7763975155279503], 'avgPrecision': 0.7799708611302815, 'recall': [0.7345679012345679, 0.7345679012345679, 0.8271604938271605, 0.8271604938271605, 0.7763975155279503], 'avgRecall': 0.7799708611302815, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T01:39:40.917572 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.6975308641975309, 0.7283950617283951, 0.7469135802469136, 0.7962962962962963, 0.7453416149068323], 'avgAccuracy': 0.7428954834751936, 'f1': [0.6352100804096146, 0.6565014487252122, 0.683743981063914, 0.7559265963262064, 0.6973026947598235], 'avgF1': 0.6857369602569542, 'precision': [0.6975308641975309, 0.7283950617283951, 0.7469135802469136, 0.7962962962962963, 0.7453416149068323], 'avgPrecision': 0.7428954834751936, 'recall': [0.6975308641975309, 0.7283950617283951, 0.7469135802469136, 0.7962962962962963, 0.7453416149068323], 'avgRecall': 0.7428954834751936, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T01:42:52.097799 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.7098765432098766, 0.7037037037037037, 0.7592592592592593, 0.8024691358024691, 0.7577639751552795], 'avgAccuracy': 0.7466145234261177, 'f1': [0.6751393215606801, 0.6591236450972424, 0.7244057536575331, 0.773405501436218, 0.7281567246138497], 'avgF1': 0.7120461892731047, 'precision': [0.7098765432098766, 0.7037037037037037, 0.7592592592592593, 0.8024691358024691, 0.7577639751552795], 'avgPrecision': 0.7466145234261177, 'recall': [0.7098765432098766, 0.7037037037037037, 0.7592592592592593, 0.8024691358024691, 0.7577639751552795], 'avgRecall': 0.7466145234261177, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.7098765432098766, 0.7037037037037037, 0.7592592592592593, 0.8024691358024691, 0.7577639751552795], 'avgAccuracy': 0.7466145234261177, 'f1': [0.6751393215606801, 0.6591236450972424, 0.7244057536575331, 0.773405501436218, 0.7281567246138497], 'avgF1': 0.7120461892731047, 'precision': [0.7098765432098766, 0.7037037037037037, 0.7592592592592593, 0.8024691358024691, 0.7577639751552795], 'avgPrecision': 0.7466145234261177, 'recall': [0.7098765432098766, 0.7037037037037037, 0.7592592592592593, 0.8024691358024691, 0.7577639751552795], 'avgRecall': 0.7466145234261177, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.805927  0.793657   0.805927  0.805927   
1  0.770064  0.747537   0.770064  0.770064   
2  0.742888  0.713065   0.742888  0.742888   
3  0.646476  0.613261   0.646476  0.646476   
4  0.704547  0.711210   0.704547  0.704547   
5  0.735381  0.733193   0.735381  0.735381   
6  0.742895  0.685737   0.742895  0.742895   
7  0.742872  0.711630   0.742872  0.742872   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T01:49:39.680436 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.7592592592592593, 0.8024691358024691, 0.8333333333333334, 0.845679012345679, 0.7701863354037267], 'avgAccuracy': 0.8021854152288935, 'f1': [0.746708399231126, 0.7841072883374068, 0.8244949494949495, 0.8270287625774391, 0.7655420820454611], 'avgF1': 0.7895762963372766, 'precision': [0.7592592592592593, 0.8024691358024691, 0.8333333333333334, 0.845679012345679, 0.7701863354037267], 'avgPrecision': 0.8021854152288935, 'recall': [0.7592592592592593, 0.8024691358024691, 0.8333333333333334, 0.845679012345679, 0.7701863354037267], 'avgRecall': 0.8021854152288935, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T01:49:45.977096 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.7469135802469136, 0.7592592592592593, 0.7839506172839507, 0.8148148148148148, 0.7639751552795031], 'avgAccuracy': 0.7737826853768883, 'f1': [0.724122013496634, 0.7266931821387267, 0.767111713053804, 0.7929223819087962, 0.7454402173353727], 'avgF1': 0.7512579015866667, 'precision': [0.7469135802469136, 0.7592592592592593, 0.7839506172839507, 0.8148148148148148, 0.7639751552795031], 'avgPrecision': 0.7737826853768883, 'recall': [0.7469135802469136, 0.7592592592592593, 0.7839506172839507, 0.8148148148148148, 0.7639751552795031], 'avgRecall': 0.7737826853768883, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 13, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T01:50:00.103039 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.7160493827160493, 0.6851851851851852, 0.7407407407407407, 0.7839506172839507, 0.7639751552795031], 'avgAccuracy': 0.7379802162410858, 'f1': [0.686086025177423, 0.6401485021212732, 0.7046711137660958, 0.7562535759742066, 0.73972128790849], 'avgF1': 0.7053761009894978, 'precision': [0.7160493827160493, 0.6851851851851852, 0.7407407407407407, 0.7839506172839507, 0.7639751552795031], 'avgPrecision': 0.7379802162410858, 'recall': [0.7160493827160493, 0.6851851851851852, 0.7407407407407407, 0.7839506172839507, 0.7639751552795031], 'avgRecall': 0.7379802162410858, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T01:50:00.493664 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.5802469135802469, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6459627329192547], 'avgAccuracy': 0.6440073613986658, 'f1': [0.5470405169982825, 0.5943597040274312, 0.6152045175846284, 0.6754538991063272, 0.6239731265983129], 'avgF1': 0.6112063528629964, 'precision': [0.5802469135802469, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6459627329192547], 'avgPrecision': 0.6440073613986658, 'recall': [0.5802469135802469, 0.6419753086419753, 0.654320987654321, 0.6975308641975309, 0.6459627329192547], 'avgRecall': 0.6440073613986658, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T01:50:06.266768 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.8148148148148148, 0.8580246913580247, 0.7962962962962963, 0.8395061728395061, 0.7391304347826086], 'avgAccuracy': 0.8095544820182501, 'f1': [0.8041864514086737, 0.8513811142635619, 0.787825521727388, 0.8264848601798486, 0.7477536156941519], 'avgF1': 0.8035263126547249, 'precision': [0.8148148148148148, 0.8580246913580247, 0.7962962962962963, 0.8395061728395061, 0.7391304347826086], 'avgPrecision': 0.8095544820182501, 'recall': [0.8148148148148148, 0.8580246913580247, 0.7962962962962963, 0.8395061728395061, 0.7391304347826086], 'avgRecall': 0.8095544820182501, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T01:50:07.080864 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.7407407407407407, 0.7160493827160493, 0.8024691358024691, 0.7962962962962963, 0.7763975155279503], 'avgAccuracy': 0.7663906142167012, 'f1': [0.7330167488503123, 0.7068458682377489, 0.7993221029356143, 0.7929188438856618, 0.7815030917057335], 'avgF1': 0.7627213311230142, 'precision': [0.7407407407407407, 0.7160493827160493, 0.8024691358024691, 0.7962962962962963, 0.7763975155279503], 'avgPrecision': 0.7663906142167012, 'recall': [0.7407407407407407, 0.7160493827160493, 0.8024691358024691, 0.7962962962962963, 0.7763975155279503], 'avgRecall': 0.7663906142167012, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T01:50:08.441915 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.6975308641975309, 0.7283950617283951, 0.7469135802469136, 0.7962962962962963, 0.7453416149068323], 'avgAccuracy': 0.7428954834751936, 'f1': [0.6352100804096146, 0.6565014487252122, 0.683743981063914, 0.7559265963262064, 0.6973026947598235], 'avgF1': 0.6857369602569542, 'precision': [0.6975308641975309, 0.7283950617283951, 0.7469135802469136, 0.7962962962962963, 0.7453416149068323], 'avgPrecision': 0.7428954834751936, 'recall': [0.6975308641975309, 0.7283950617283951, 0.7469135802469136, 0.7962962962962963, 0.7453416149068323], 'avgRecall': 0.7428954834751936, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T01:53:12.899191 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.7098765432098766, 0.7160493827160493, 0.7592592592592593, 0.7839506172839507, 0.7639751552795031], 'avgAccuracy': 0.7466221915497278, 'f1': [0.679818580909617, 0.6754562337188422, 0.7261425167857916, 0.7531000723452593, 0.7442517886985416], 'avgF1': 0.7157538384916103, 'precision': [0.7098765432098766, 0.7160493827160493, 0.7592592592592593, 0.7839506172839507, 0.7639751552795031], 'avgPrecision': 0.7466221915497278, 'recall': [0.7098765432098766, 0.7160493827160493, 0.7592592592592593, 0.7839506172839507, 0.7639751552795031], 'avgRecall': 0.7466221915497278, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.7098765432098766, 0.7160493827160493, 0.7592592592592593, 0.7839506172839507, 0.7639751552795031], 'avgAccuracy': 0.7466221915497278, 'f1': [0.679818580909617, 0.6754562337188422, 0.7261425167857916, 0.7531000723452593, 0.7442517886985416], 'avgF1': 0.7157538384916103, 'precision': [0.7098765432098766, 0.7160493827160493, 0.7592592592592593, 0.7839506172839507, 0.7639751552795031], 'avgPrecision': 0.7466221915497278, 'recall': [0.7098765432098766, 0.7160493827160493, 0.7592592592592593, 0.7839506172839507, 0.7639751552795031], 'avgRecall': 0.7466221915497278, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.799709  0.787139   0.799709  0.799709   
1  0.767587  0.744063   0.767587  0.767587   
2  0.733034  0.701133   0.733034  0.733034   
3  0.644007  0.611206   0.644007  0.644007   
4  0.702109  0.712469   0.702109  0.702109   
5  0.740350  0.735131   0.740350  0.740350   
6  0.742895  0.685737   0.742895  0.742895   
7  0.742918  0.709405   0.742918  0.742918   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T01:59:57.878778 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.7716049382716049, 0.808641975308642, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgAccuracy': 0.810850394908366, 'f1': [0.7601548377771971, 0.7955156760425948, 0.8226896442791828, 0.8356846573456541, 0.7836374604168117], 'avgF1': 0.7995364551722881, 'precision': [0.7716049382716049, 0.808641975308642, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgPrecision': 0.810850394908366, 'recall': [0.7716049382716049, 0.808641975308642, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgRecall': 0.810850394908366, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T02:00:04.108972 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.7407407407407407, 0.7530864197530864, 0.7962962962962963, 0.808641975308642, 0.7515527950310559], 'avgAccuracy': 0.7700636454259643, 'f1': [0.7184661543906541, 0.7216468322260118, 0.7802979987547096, 0.7837354373152495, 0.7334685857880249], 'avgF1': 0.74752300169493, 'precision': [0.7407407407407407, 0.7530864197530864, 0.7962962962962963, 0.808641975308642, 0.7515527950310559], 'avgPrecision': 0.7700636454259643, 'recall': [0.7407407407407407, 0.7530864197530864, 0.7962962962962963, 0.808641975308642, 0.7515527950310559], 'avgRecall': 0.7700636454259643, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 17, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T02:00:18.137552 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.7098765432098766, 0.6851851851851852, 0.7407407407407407, 0.7777777777777778, 0.7639751552795031], 'avgAccuracy': 0.7355110804386167, 'f1': [0.6764529515509907, 0.6401485021212732, 0.7046711137660958, 0.7463612116389893, 0.73972128790849], 'avgF1': 0.7014710133971678, 'precision': [0.7098765432098766, 0.6851851851851852, 0.7407407407407407, 0.7777777777777778, 0.7639751552795031], 'avgPrecision': 0.7355110804386167, 'recall': [0.7098765432098766, 0.6851851851851852, 0.7407407407407407, 0.7777777777777778, 0.7639751552795031], 'avgRecall': 0.7355110804386167, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T02:00:18.512601 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.5987654320987654, 0.6358024691358025, 0.6481481481481481, 0.691358024691358, 0.639751552795031], 'avgAccuracy': 0.642765125373821, 'f1': [0.5611118783197918, 0.5893428636775743, 0.609826762246117, 0.6701478054223279, 0.6188975483103929], 'avgF1': 0.6098653715952408, 'precision': [0.5987654320987654, 0.6358024691358025, 0.6481481481481481, 0.691358024691358, 0.639751552795031], 'avgPrecision': 0.642765125373821, 'recall': [0.5987654320987654, 0.6358024691358025, 0.6481481481481481, 0.691358024691358, 0.639751552795031], 'avgRecall': 0.642765125373821, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T02:00:24.825026 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.8209876543209876, 0.7716049382716049, 0.8148148148148148, 0.808641975308642, 0.7391304347826086], 'avgAccuracy': 0.7910359634997316, 'f1': [0.8117837923393479, 0.7538514307465476, 0.8041994014216236, 0.7830021487428895, 0.7477536156941519], 'avgF1': 0.7801180777889121, 'precision': [0.8209876543209876, 0.7716049382716049, 0.8148148148148148, 0.808641975308642, 0.7391304347826086], 'avgPrecision': 0.7910359634997316, 'recall': [0.8209876543209876, 0.7716049382716049, 0.8148148148148148, 0.808641975308642, 0.7391304347826086], 'avgRecall': 0.7910359634997316, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T02:00:25.616581 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.7469135802469136, 0.8209876543209876, 0.7469135802469136, 0.8271604938271605, 0.7391304347826086], 'avgAccuracy': 0.7762211486849168, 'f1': [0.748433060707567, 0.8177821506938099, 0.7517237307449326, 0.8173320980917929, 0.7466267905638274], 'avgF1': 0.776379566160386, 'precision': [0.7469135802469136, 0.8209876543209876, 0.7469135802469136, 0.8271604938271605, 0.7391304347826086], 'avgPrecision': 0.7762211486849168, 'recall': [0.7469135802469136, 0.8209876543209876, 0.7469135802469136, 0.8271604938271605, 0.7391304347826086], 'avgRecall': 0.7762211486849168, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T02:00:27.057333 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.6975308641975309, 0.7283950617283951, 0.7283950617283951, 0.7962962962962963, 0.7453416149068323], 'avgAccuracy': 0.7391917797714899, 'f1': [0.6352100804096146, 0.6565014487252122, 0.6665423930921565, 0.7559265963262064, 0.6973026947598235], 'avgF1': 0.6822966426626026, 'precision': [0.6975308641975309, 0.7283950617283951, 0.7283950617283951, 0.7962962962962963, 0.7453416149068323], 'avgPrecision': 0.7391917797714899, 'recall': [0.6975308641975309, 0.7283950617283951, 0.7283950617283951, 0.7962962962962963, 0.7453416149068323], 'avgRecall': 0.7391917797714899, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T02:03:38.399446 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.6975308641975309, 0.7098765432098766, 0.7530864197530864, 0.7962962962962963, 0.7701863354037267], 'avgAccuracy': 0.7453952917721034, 'f1': [0.665352477379186, 0.6710924218227611, 0.7214383695865177, 0.7626487136073415, 0.744276841171251], 'avgF1': 0.7129617647134114, 'precision': [0.6975308641975309, 0.7098765432098766, 0.7530864197530864, 0.7962962962962963, 0.7701863354037267], 'avgPrecision': 0.7453952917721034, 'recall': [0.6975308641975309, 0.7098765432098766, 0.7530864197530864, 0.7962962962962963, 0.7701863354037267], 'avgRecall': 0.7453952917721034, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.7716049382716049, 0.808641975308642, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgAccuracy': 0.810850394908366, 'f1': [0.7601548377771971, 0.7955156760425948, 0.8226896442791828, 0.8356846573456541, 0.7836374604168117], 'avgF1': 0.7995364551722881, 'precision': [0.7716049382716049, 0.808641975308642, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgPrecision': 0.810850394908366, 'recall': [0.7716049382716049, 0.808641975308642, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgRecall': 0.810850394908366, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.800951  0.789327   0.800951  0.800951   
1  0.770064  0.747523   0.770064  0.770064   
2  0.730565  0.697177   0.730565  0.730565   
3  0.642765  0.609865   0.642765  0.642765   
4  0.714385  0.719180   0.714385  0.714385   
5  0.733011  0.728310   0.733011  0.733011   
6  0.739192  0.682297   0.739192  0.739192   
7  0.739215  0.708310   0.739215  0.739215   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T02:10:30.799441 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.7654320987654321, 0.8024691358024691, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgAccuracy': 0.8083812591058968, 'f1': [0.7546542132713443, 0.7899892304320515, 0.8226896442791828, 0.8356846573456541, 0.7836374604168117], 'avgF1': 0.7973310411490089, 'precision': [0.7654320987654321, 0.8024691358024691, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgPrecision': 0.8083812591058968, 'recall': [0.7654320987654321, 0.8024691358024691, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgRecall': 0.8083812591058968, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T02:10:37.154653 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.7469135802469136, 0.7716049382716049, 0.7962962962962963, 0.808641975308642, 0.7515527950310559], 'avgAccuracy': 0.7750019170309025, 'f1': [0.7303634416729655, 0.7493853882742771, 0.7802979987547096, 0.7837354373152495, 0.7334685857880249], 'avgF1': 0.7554501703610453, 'precision': [0.7469135802469136, 0.7716049382716049, 0.7962962962962963, 0.808641975308642, 0.7515527950310559], 'avgPrecision': 0.7750019170309025, 'recall': [0.7469135802469136, 0.7716049382716049, 0.7962962962962963, 0.808641975308642, 0.7515527950310559], 'avgRecall': 0.7750019170309025, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T02:10:51.052067 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.7098765432098766, 0.691358024691358, 0.7407407407407407, 0.7716049382716049, 0.7639751552795031], 'avgAccuracy': 0.7355110804386167, 'f1': [0.6764529515509907, 0.650900558541884, 0.7046711137660958, 0.7415680294757552, 0.73972128790849], 'avgF1': 0.7026627882486431, 'precision': [0.7098765432098766, 0.691358024691358, 0.7407407407407407, 0.7716049382716049, 0.7639751552795031], 'avgPrecision': 0.7355110804386167, 'recall': [0.7098765432098766, 0.691358024691358, 0.7407407407407407, 0.7716049382716049, 0.7639751552795031], 'avgRecall': 0.7355110804386167, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-29T02:10:51.489567 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.5864197530864198, 0.5925925925925926, 0.6419753086419753, 0.6790123456790124, 0.6211180124223602], 'avgAccuracy': 0.6242236024844721, 'f1': [0.5809409163814105, 0.5728233680303396, 0.6326288320607655, 0.6751532672966412, 0.6294840917733553], 'avgF1': 0.6182060951085024, 'precision': [0.5864197530864198, 0.5925925925925926, 0.6419753086419753, 0.6790123456790124, 0.6211180124223602], 'avgPrecision': 0.6242236024844721, 'recall': [0.5864197530864198, 0.5925925925925926, 0.6419753086419753, 0.6790123456790124, 0.6211180124223602], 'avgRecall': 0.6242236024844721, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T02:10:57.783436 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.808641975308642, 0.7592592592592593, 0.7654320987654321, 0.8024691358024691, 0.8198757763975155], 'avgAccuracy': 0.7911356491066636, 'f1': [0.7937713257646256, 0.7236540899555273, 0.7491276945086626, 0.7820926817611987, 0.8152308218097692], 'avgF1': 0.7727753227599566, 'precision': [0.808641975308642, 0.7592592592592593, 0.7654320987654321, 0.8024691358024691, 0.8198757763975155], 'avgPrecision': 0.7911356491066636, 'recall': [0.808641975308642, 0.7592592592592593, 0.7654320987654321, 0.8024691358024691, 0.8198757763975155], 'avgRecall': 0.7911356491066636, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T02:10:58.628100 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.7469135802469136, 0.808641975308642, 0.8024691358024691, 0.8271604938271605, 0.7267080745341615], 'avgAccuracy': 0.7823786519438694, 'f1': [0.7461058831429201, 0.8026561915450804, 0.8068701253461337, 0.8105383581222353, 0.7252527418521415], 'avgF1': 0.7782846600017022, 'precision': [0.7469135802469136, 0.808641975308642, 0.8024691358024691, 0.8271604938271605, 0.7267080745341615], 'avgPrecision': 0.7823786519438694, 'recall': [0.7469135802469136, 0.808641975308642, 0.8024691358024691, 0.8271604938271605, 0.7267080745341615], 'avgRecall': 0.7823786519438694, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T02:11:00.214255 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.6975308641975309, 0.7283950617283951, 0.7283950617283951, 0.7962962962962963, 0.7453416149068323], 'avgAccuracy': 0.7391917797714899, 'f1': [0.6352100804096146, 0.6565014487252122, 0.6665423930921565, 0.7559265963262064, 0.6973026947598235], 'avgF1': 0.6822966426626026, 'precision': [0.6975308641975309, 0.7283950617283951, 0.7283950617283951, 0.7962962962962963, 0.7453416149068323], 'avgPrecision': 0.7391917797714899, 'recall': [0.6975308641975309, 0.7283950617283951, 0.7283950617283951, 0.7962962962962963, 0.7453416149068323], 'avgRecall': 0.7391917797714899, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T02:14:05.931225 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.6975308641975309, 0.7222222222222222, 0.7530864197530864, 0.7901234567901234, 0.7701863354037267], 'avgAccuracy': 0.746629859673338, 'f1': [0.665352477379186, 0.6855492244381134, 0.7214383695865177, 0.7578536379276576, 0.7493753963203624], 'avgF1': 0.7159138211303674, 'precision': [0.6975308641975309, 0.7222222222222222, 0.7530864197530864, 0.7901234567901234, 0.7701863354037267], 'avgPrecision': 0.746629859673338, 'recall': [0.6975308641975309, 0.7222222222222222, 0.7530864197530864, 0.7901234567901234, 0.7701863354037267], 'avgRecall': 0.746629859673338, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.7654320987654321, 0.8024691358024691, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgAccuracy': 0.8083812591058968, 'f1': [0.7546542132713443, 0.7899892304320515, 0.8226896442791828, 0.8356846573456541, 0.7836374604168117], 'avgF1': 0.7973310411490089, 'precision': [0.7654320987654321, 0.8024691358024691, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgPrecision': 0.8083812591058968, 'recall': [0.7654320987654321, 0.8024691358024691, 0.8333333333333334, 0.8518518518518519, 0.7888198757763976], 'avgRecall': 0.8083812591058968, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.804662  0.792529   0.804662  0.804662   
1  0.772533  0.751797   0.772533  0.772533   
2  0.730565  0.698338   0.730565  0.730565   
3  0.624224  0.618206   0.624224  0.624224   
4  0.714385  0.719180   0.714385  0.714385   
5  0.731677  0.727523   0.731677  0.731677   
6  0.739192  0.682297   0.739192  0.739192   
7  0.741699  0.712373   0.741699  0.741699   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T02:20:57.220847 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.7654320987654321, 0.8209876543209876, 0.8395061728395061, 0.845679012345679, 0.782608695652174], 'avgAccuracy': 0.8108427267847558, 'f1': [0.7546542132713443, 0.8093165330278732, 0.8285633907605032, 0.8270287625774391, 0.7812639756646431], 'avgF1': 0.8001653750603606, 'precision': [0.7654320987654321, 0.8209876543209876, 0.8395061728395061, 0.845679012345679, 0.782608695652174], 'avgPrecision': 0.8108427267847558, 'recall': [0.7654320987654321, 0.8209876543209876, 0.8395061728395061, 0.845679012345679, 0.782608695652174], 'avgRecall': 0.8108427267847558, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T02:21:03.612589 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.7530864197530864, 0.7716049382716049, 0.7962962962962963, 0.8148148148148148, 0.7453416149068323], 'avgAccuracy': 0.776228816808527, 'f1': [0.7356569515692472, 0.7493853882742771, 0.7802979987547096, 0.7886980753919227, 0.7240699499835712], 'avgF1': 0.7556216727947456, 'precision': [0.7530864197530864, 0.7716049382716049, 0.7962962962962963, 0.8148148148148148, 0.7453416149068323], 'avgPrecision': 0.776228816808527, 'recall': [0.7530864197530864, 0.7716049382716049, 0.7962962962962963, 0.8148148148148148, 0.7453416149068323], 'avgRecall': 0.776228816808527, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T02:21:17.629848 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.7098765432098766, 0.6975308641975309, 0.7407407407407407, 0.7716049382716049, 0.7577639751552795], 'avgAccuracy': 0.7355034123150065, 'f1': [0.6764529515509907, 0.661345833128804, 0.7046711137660958, 0.7415680294757552, 0.7289882136429451], 'avgF1': 0.7026052283129182, 'precision': [0.7098765432098766, 0.6975308641975309, 0.7407407407407407, 0.7716049382716049, 0.7577639751552795], 'avgPrecision': 0.7355034123150065, 'recall': [0.7098765432098766, 0.6975308641975309, 0.7407407407407407, 0.7716049382716049, 0.7577639751552795], 'avgRecall': 0.7355034123150065, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T02:21:18.036099 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.5925925925925926, 0.5925925925925926, 0.6358024691358025, 0.691358024691358, 0.6335403726708074], 'avgAccuracy': 0.6291772103366307, 'f1': [0.5864947019335793, 0.5680620253532349, 0.6243603310269977, 0.6832949710539165, 0.6406757896245185], 'avgF1': 0.6205775637984494, 'precision': [0.5925925925925926, 0.5925925925925926, 0.6358024691358025, 0.691358024691358, 0.6335403726708074], 'avgPrecision': 0.6291772103366307, 'recall': [0.5925925925925926, 0.5925925925925926, 0.6358024691358025, 0.691358024691358, 0.6335403726708074], 'avgRecall': 0.6291772103366307, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T02:21:24.676411 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.8024691358024691, 0.7592592592592593, 0.7654320987654321, 0.8024691358024691, 0.8198757763975155], 'avgAccuracy': 0.789901081205429, 'f1': [0.7881019268575218, 0.7236540899555273, 0.7491276945086626, 0.7820926817611987, 0.8152308218097692], 'avgF1': 0.7716414429785359, 'precision': [0.8024691358024691, 0.7592592592592593, 0.7654320987654321, 0.8024691358024691, 0.8198757763975155], 'avgPrecision': 0.789901081205429, 'recall': [0.8024691358024691, 0.7592592592592593, 0.7654320987654321, 0.8024691358024691, 0.8198757763975155], 'avgRecall': 0.789901081205429, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-29T02:21:25.472647 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.7469135802469136, 0.7530864197530864, 0.7962962962962963, 0.8148148148148148, 0.7639751552795031], 'avgAccuracy': 0.7750172532781229, 'f1': [0.7409822352425582, 0.745940029259154, 0.7962962962962963, 0.8092723102469691, 0.7682410754236977], 'avgF1': 0.772146389293735, 'precision': [0.7469135802469136, 0.7530864197530864, 0.7962962962962963, 0.8148148148148148, 0.7639751552795031], 'avgPrecision': 0.7750172532781229, 'recall': [0.7469135802469136, 0.7530864197530864, 0.7962962962962963, 0.8148148148148148, 0.7639751552795031], 'avgRecall': 0.7750172532781229, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-29T02:21:26.869741 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.6975308641975309, 0.7283950617283951, 0.7283950617283951, 0.7962962962962963, 0.7453416149068323], 'avgAccuracy': 0.7391917797714899, 'f1': [0.6352100804096146, 0.6565014487252122, 0.6665423930921565, 0.7559265963262064, 0.6973026947598235], 'avgF1': 0.6822966426626026, 'precision': [0.6975308641975309, 0.7283950617283951, 0.7283950617283951, 0.7962962962962963, 0.7453416149068323], 'avgPrecision': 0.7391917797714899, 'recall': [0.6975308641975309, 0.7283950617283951, 0.7283950617283951, 0.7962962962962963, 0.7453416149068323], 'avgRecall': 0.7391917797714899, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-29T02:24:29.738269 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.7037037037037037, 0.7160493827160493, 0.7345679012345679, 0.7962962962962963, 0.7763975155279503], 'avgAccuracy': 0.7454029598957135, 'f1': [0.6790045510992385, 0.6754562337188422, 0.6987706131488984, 0.7626487136073415, 0.7494819783495524], 'avgF1': 0.7130724179847746, 'precision': [0.7037037037037037, 0.7160493827160493, 0.7345679012345679, 0.7962962962962963, 0.7763975155279503], 'avgPrecision': 0.7454029598957135, 'recall': [0.7037037037037037, 0.7160493827160493, 0.7345679012345679, 0.7962962962962963, 0.7763975155279503], 'avgRecall': 0.7454029598957135, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.7654320987654321, 0.8209876543209876, 0.8395061728395061, 0.845679012345679, 0.782608695652174], 'avgAccuracy': 0.8108427267847558, 'f1': [0.7546542132713443, 0.8093165330278732, 0.8285633907605032, 0.8270287625774391, 0.7812639756646431], 'avgF1': 0.8001653750603606, 'precision': [0.7654320987654321, 0.8209876543209876, 0.8395061728395061, 0.845679012345679, 0.782608695652174], 'avgPrecision': 0.8108427267847558, 'recall': [0.7654320987654321, 0.8209876543209876, 0.8395061728395061, 0.845679012345679, 0.782608695652174], 'avgRecall': 0.8108427267847558, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.802193  0.791963   0.802193  0.802193   
1  0.771291  0.750884   0.771291  0.771291   
2  0.734269  0.704081   0.734269  0.734269   
3  0.629177  0.620578   0.629177  0.629177   
4  0.685990  0.695333   0.685990  0.685990   
5  0.736700  0.731715   0.736700  0.736700   
6  0.739192  0.682297   0.739192  0.739192   
7  0.740449  0.707747   0.740449  0.740449   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T02:32:21.304093 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.7839506172839507, 0.7962962962962963, 0.8333333333333334, 0.845679012345679, 0.782608695652174], 'avgAccuracy': 0.8083735909822867, 'f1': [0.7712258316748818, 0.7817148190573161, 0.8221770092330599, 0.8270287625774391, 0.7812639756646431], 'avgF1': 0.796682079641468, 'precision': [0.7839506172839507, 0.7962962962962963, 0.8333333333333334, 0.845679012345679, 0.782608695652174], 'avgPrecision': 0.8083735909822867, 'recall': [0.7839506172839507, 0.7962962962962963, 0.8333333333333334, 0.845679012345679, 0.782608695652174], 'avgRecall': 0.8083735909822867, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-29T02:32:27.616471 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.7407407407407407, 0.7530864197530864, 0.7839506172839507, 0.7962962962962963, 0.7329192546583851], 'avgAccuracy': 0.7613986657464918, 'f1': [0.7146791804734437, 0.7335683358282229, 0.7703289891408993, 0.7739383657480546, 0.7100751913116495], 'avgF1': 0.740518012500454, 'precision': [0.7407407407407407, 0.7530864197530864, 0.7839506172839507, 0.7962962962962963, 0.7329192546583851], 'avgPrecision': 0.7613986657464918, 'recall': [0.7407407407407407, 0.7530864197530864, 0.7839506172839507, 0.7962962962962963, 0.7329192546583851], 'avgRecall': 0.7613986657464918, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 17, 'p': 2, 'weights': 'distance'}]}
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Processing Model: LogisticRegression
2021-05-29T02:32:41.596687 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.7098765432098766, 0.6604938271604939, 0.7345679012345679, 0.7592592592592593, 0.7515527950310559], 'avgAccuracy': 0.7231500651790507, 'f1': [0.6713390051181347, 0.6160771146242938, 0.6945224740802547, 0.7145427519308508, 0.7241293686331963], 'avgF1': 0.6841221428773461, 'precision': [0.7098765432098766, 0.6604938271604939, 0.7345679012345679, 0.7592592592592593, 0.7515527950310559], 'avgPrecision': 0.7231500651790507, 'recall': [0.7098765432098766, 0.6604938271604939, 0.7345679012345679, 0.7592592592592593, 0.7515527950310559], 'avgRecall': 0.7231500651790507, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-29T02:32:41.971686 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.5987654320987654, 0.5740740740740741, 0.6111111111111112, 0.654320987654321, 0.5838509316770186], 'avgAccuracy': 0.6044245073230581, 'f1': [0.604291789013086, 0.5568425465606943, 0.6040487963321612, 0.6529900741636979, 0.593124084025403], 'avgF1': 0.6022594580190085, 'precision': [0.5987654320987654, 0.5740740740740741, 0.6111111111111112, 0.654320987654321, 0.5838509316770186], 'avgPrecision': 0.6044245073230581, 'recall': [0.5987654320987654, 0.5740740740740741, 0.6111111111111112, 0.654320987654321, 0.5838509316770186], 'avgRecall': 0.6044245073230581, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-29T02:32:48.230338 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.8024691358024691, 0.7592592592592593, 0.7592592592592593, 0.8024691358024691, 0.8198757763975155], 'avgAccuracy': 0.7886665133041945, 'f1': [0.7881019268575218, 0.7236540899555273, 0.7242798353909465, 0.7820926817611987, 0.8152308218097692], 'avgF1': 0.7666718711549927, 'precision': [0.8024691358024691, 0.7592592592592593, 0.7592592592592593, 0.8024691358024691, 0.8198757763975155], 'avgPrecision': 0.7886665133041945, 'recall': [0.8024691358024691, 0.7592592592592593, 0.7592592592592593, 0.8024691358024691, 0.8198757763975155], 'avgRecall': 0.7886665133041945, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-29T02:32:48.983768 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.7716049382716049, 0.7839506172839507, 0.8024691358024691, 0.7592592592592593, 0.7018633540372671], 'avgAccuracy': 0.7638294609309102, 'f1': [0.7674663054430882, 0.7707853789742435, 0.79221753375115, 0.7516859966591601, 0.7055053642010164], 'avgF1': 0.7575321158057317, 'precision': [0.7716049382716049, 0.7839506172839507, 0.8024691358024691, 0.7592592592592593, 0.7018633540372671], 'avgPrecision': 0.7638294609309102, 'recall': [0.7716049382716049, 0.7839506172839507, 0.8024691358024691, 0.7592592592592593, 0.7018633540372671], 'avgRecall': 0.7638294609309102, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
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Processing Model: SVC
2021-05-29T02:32:50.539560 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.691358024691358, 0.7098765432098766, 0.7160493827160493, 0.7654320987654321, 0.7329192546583851], 'avgAccuracy': 0.7231270608082202, 'f1': [0.5942518165275348, 0.6155436329371947, 0.6302635930272691, 0.6846829746680269, 0.6692236845837045], 'avgF1': 0.638793140348746, 'precision': [0.691358024691358, 0.7098765432098766, 0.7160493827160493, 0.7654320987654321, 0.7329192546583851], 'avgPrecision': 0.7231270608082202, 'recall': [0.691358024691358, 0.7098765432098766, 0.7160493827160493, 0.7654320987654321, 0.7329192546583851], 'avgRecall': 0.7231270608082202, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-29T02:35:56.776080 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.7037037037037037, 0.6975308641975309, 0.7407407407407407, 0.7777777777777778, 0.7701863354037267], 'avgAccuracy': 0.737987884364696, 'f1': [0.665198472623671, 0.6499470317402112, 0.7016291290362064, 0.7371109372283081, 0.7467984553298054], 'avgF1': 0.7001368051916405, 'precision': [0.7037037037037037, 0.6975308641975309, 0.7407407407407407, 0.7777777777777778, 0.7701863354037267], 'avgPrecision': 0.737987884364696, 'recall': [0.7037037037037037, 0.6975308641975309, 0.7407407407407407, 0.7777777777777778, 0.7701863354037267], 'avgRecall': 0.737987884364696, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.7839506172839507, 0.7962962962962963, 0.8333333333333334, 0.845679012345679, 0.782608695652174], 'avgAccuracy': 0.8083735909822867, 'f1': [0.7712258316748818, 0.7817148190573161, 0.8221770092330599, 0.8270287625774391, 0.7812639756646431], 'avgF1': 0.796682079641468, 'precision': [0.7839506172839507, 0.7962962962962963, 0.8333333333333334, 0.845679012345679, 0.782608695652174], 'avgPrecision': 0.8083735909822867, 'recall': [0.7839506172839507, 0.7962962962962963, 0.8333333333333334, 0.845679012345679, 0.782608695652174], 'avgRecall': 0.8083735909822867, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.804670  0.792247   0.804670  0.804670   
1  0.761399  0.740518   0.761399  0.761399   
2  0.719446  0.680886   0.719446  0.719446   
3  0.604425  0.602259   0.604425  0.604425   
4  0.682348  0.692229   0.682348  0.682348   
5  0.729216  0.723506   0.729216  0.729216   
6  0.723127  0.638793   0.723127  0.723127   
7  0.725619  0.688095   0.725619  0.725619   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T02:42:50.386131 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.7839506172839507, 0.7901234567901234, 0.8333333333333334, 0.8395061728395061, 0.7701863354037267], 'avgAccuracy': 0.8034199831301281, 'f1': [0.7712258316748818, 0.7762855581876504, 0.8201395429202776, 0.8181308522693387, 0.7710515744260066], 'avgF1': 0.791366671895631, 'precision': [0.7839506172839507, 0.7901234567901234, 0.8333333333333334, 0.8395061728395061, 0.7701863354037267], 'avgPrecision': 0.8034199831301281, 'recall': [0.7839506172839507, 0.7901234567901234, 0.8333333333333334, 0.8395061728395061, 0.7701863354037267], 'avgRecall': 0.8034199831301281, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T02:42:56.679410 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.7222222222222222, 0.7469135802469136, 0.7777777777777778, 0.7901234567901234, 0.7391304347826086], 'avgAccuracy': 0.7552334943639292, 'f1': [0.6865576477504581, 0.7283483942132282, 0.76538191307839, 0.7598422658343588, 0.7196314642332993], 'avgF1': 0.7319523370219468, 'precision': [0.7222222222222222, 0.7469135802469136, 0.7777777777777778, 0.7901234567901234, 0.7391304347826086], 'avgPrecision': 0.7552334943639292, 'recall': [0.7222222222222222, 0.7469135802469136, 0.7777777777777778, 0.7901234567901234, 0.7391304347826086], 'avgRecall': 0.7552334943639292, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 17, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T02:43:10.483794 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.7037037037037037, 0.6604938271604939, 0.7345679012345679, 0.7592592592592593, 0.7515527950310559], 'avgAccuracy': 0.7219154972778161, 'f1': [0.6665789688909384, 0.6212074758606062, 0.6945224740802547, 0.7145427519308508, 0.7266737912788128], 'avgF1': 0.6847050924082926, 'precision': [0.7037037037037037, 0.6604938271604939, 0.7345679012345679, 0.7592592592592593, 0.7515527950310559], 'avgPrecision': 0.7219154972778161, 'recall': [0.7037037037037037, 0.6604938271604939, 0.7345679012345679, 0.7592592592592593, 0.7515527950310559], 'avgRecall': 0.7219154972778161, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T02:43:10.858797 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.5925925925925926, 0.5679012345679012, 0.6296296296296297, 0.6419753086419753, 0.5838509316770186], 'avgAccuracy': 0.6031899394218234, 'f1': [0.5980762023224868, 0.5570668212645697, 0.6238812667345829, 0.6393884171191424, 0.593124084025403], 'avgF1': 0.602307358293237, 'precision': [0.5925925925925926, 0.5679012345679012, 0.6296296296296297, 0.6419753086419753, 0.5838509316770186], 'avgPrecision': 0.6031899394218234, 'recall': [0.5925925925925926, 0.5679012345679012, 0.6296296296296297, 0.6419753086419753, 0.5838509316770186], 'avgRecall': 0.6031899394218234, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T02:43:16.959149 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.8024691358024691, 0.7592592592592593, 0.7962962962962963, 0.7839506172839507, 0.8198757763975155], 'avgAccuracy': 0.7923702170078982, 'f1': [0.7881019268575218, 0.7236540899555273, 0.7901242419638451, 0.7590139893724943, 0.8152308218097692], 'avgF1': 0.7752250139918315, 'precision': [0.8024691358024691, 0.7592592592592593, 0.7962962962962963, 0.7839506172839507, 0.8198757763975155], 'avgPrecision': 0.7923702170078982, 'recall': [0.8024691358024691, 0.7592592592592593, 0.7962962962962963, 0.7839506172839507, 0.8198757763975155], 'avgRecall': 0.7923702170078982, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T02:43:17.811302 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.7345679012345679, 0.7407407407407407, 0.7839506172839507, 0.7962962962962963, 0.7515527950310559], 'avgAccuracy': 0.7614216701173223, 'f1': [0.7279782654948364, 0.7349560429506534, 0.7817395979350684, 0.778868380665227, 0.7570569028639513], 'avgF1': 0.7561198379819473, 'precision': [0.7345679012345679, 0.7407407407407407, 0.7839506172839507, 0.7962962962962963, 0.7515527950310559], 'avgPrecision': 0.7614216701173223, 'recall': [0.7345679012345679, 0.7407407407407407, 0.7839506172839507, 0.7962962962962963, 0.7515527950310559], 'avgRecall': 0.7614216701173223, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T02:43:19.647865 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.6851851851851852, 0.7098765432098766, 0.7160493827160493, 0.7654320987654321, 0.7329192546583851], 'avgAccuracy': 0.7218924929069856, 'f1': [0.5891363404072192, 0.6155436329371947, 0.6302635930272691, 0.6846829746680269, 0.6692236845837045], 'avgF1': 0.6377700451246828, 'precision': [0.6851851851851852, 0.7098765432098766, 0.7160493827160493, 0.7654320987654321, 0.7329192546583851], 'avgPrecision': 0.7218924929069856, 'recall': [0.6851851851851852, 0.7098765432098766, 0.7160493827160493, 0.7654320987654321, 0.7329192546583851], 'avgRecall': 0.7218924929069856, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T02:46:26.673596 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.7037037037037037, 0.691358024691358, 0.7407407407407407, 0.7716049382716049, 0.7639751552795031], 'avgAccuracy': 0.7342765125373821, 'f1': [0.665198472623671, 0.6513219681761553, 0.7016291290362064, 0.726159878091196, 0.7342660051228583], 'avgF1': 0.6957150906100175, 'precision': [0.7037037037037037, 0.691358024691358, 0.7407407407407407, 0.7716049382716049, 0.7639751552795031], 'avgPrecision': 0.7342765125373821, 'recall': [0.7037037037037037, 0.691358024691358, 0.7407407407407407, 0.7716049382716049, 0.7639751552795031], 'avgRecall': 0.7342765125373821, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.7037037037037037, 0.691358024691358, 0.7407407407407407, 0.7716049382716049, 0.7639751552795031], 'avgAccuracy': 0.7342765125373821, 'f1': [0.665198472623671, 0.6513219681761553, 0.7016291290362064, 0.726159878091196, 0.7342660051228583], 'avgF1': 0.6957150906100175, 'precision': [0.7037037037037037, 0.691358024691358, 0.7407407407407407, 0.7716049382716049, 0.7639751552795031], 'avgPrecision': 0.7342765125373821, 'recall': [0.7037037037037037, 0.691358024691358, 0.7407407407407407, 0.7716049382716049, 0.7639751552795031], 'avgRecall': 0.7342765125373821, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.789824  0.776871   0.789824  0.789824   
1  0.755233  0.731952   0.755233  0.755233   
2  0.718196  0.681737   0.718196  0.718196   
3  0.603190  0.602307   0.603190  0.603190   
4  0.673652  0.685748   0.673652  0.673652   
5  0.721808  0.719444   0.721808  0.721808   
6  0.721892  0.637770   0.721892  0.721892   
7  0.734277  0.695715   0.734277  0.734277   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T02:53:14.936154 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.6975308641975309, 0.6234567901234568, 0.7037037037037037, 0.7222222222222222, 0.639751552795031], 'avgAccuracy': 0.6773330266083889, 'f1': [0.6742774567099377, 0.6062065984546604, 0.6670215046629968, 0.7030467546493573, 0.6294074078460584], 'avgF1': 0.6559919444646021, 'precision': [0.6975308641975309, 0.6234567901234568, 0.7037037037037037, 0.7222222222222222, 0.639751552795031], 'avgPrecision': 0.6773330266083889, 'recall': [0.6975308641975309, 0.6234567901234568, 0.7037037037037037, 0.7222222222222222, 0.639751552795031], 'avgRecall': 0.6773330266083889, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T02:53:21.102499 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.654320987654321, 0.6481481481481481, 0.691358024691358, 0.6975308641975309, 0.6708074534161491], 'avgAccuracy': 0.6724330956215014, 'f1': [0.6229947904795442, 0.616488356982033, 0.6510127131930987, 0.6622910552735115, 0.6408794226383128], 'avgF1': 0.6387332677133001, 'precision': [0.654320987654321, 0.6481481481481481, 0.691358024691358, 0.6975308641975309, 0.6708074534161491], 'avgPrecision': 0.6724330956215014, 'recall': [0.654320987654321, 0.6481481481481481, 0.691358024691358, 0.6975308641975309, 0.6708074534161491], 'avgRecall': 0.6724330956215014, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 17, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T02:53:35.255274 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.654320987654321, 0.6111111111111112, 0.6851851851851852, 0.6975308641975309, 0.6770186335403726], 'avgAccuracy': 0.6650333563377042, 'f1': [0.6282657238550529, 0.5813482097969886, 0.65354622203565, 0.6620274645065085, 0.6656380479468935], 'avgF1': 0.6381651336282187, 'precision': [0.654320987654321, 0.6111111111111112, 0.6851851851851852, 0.6975308641975309, 0.6770186335403726], 'avgPrecision': 0.6650333563377042, 'recall': [0.654320987654321, 0.6111111111111112, 0.6851851851851852, 0.6975308641975309, 0.6770186335403726], 'avgRecall': 0.6650333563377042, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T02:53:35.630278 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.5925925925925926, 0.5679012345679012, 0.6296296296296297, 0.6419753086419753, 0.5838509316770186], 'avgAccuracy': 0.6031899394218234, 'f1': [0.5980762023224868, 0.5570668212645697, 0.6238812667345829, 0.6418398432343551, 0.593124084025403], 'avgF1': 0.6027976435162795, 'precision': [0.5925925925925926, 0.5679012345679012, 0.6296296296296297, 0.6419753086419753, 0.5838509316770186], 'avgPrecision': 0.6031899394218234, 'recall': [0.5925925925925926, 0.5679012345679012, 0.6296296296296297, 0.6419753086419753, 0.5838509316770186], 'avgRecall': 0.6031899394218234, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-29T02:53:42.223857 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.654320987654321, 0.6296296296296297, 0.6666666666666666, 0.7037037037037037, 0.546583850931677], 'avgAccuracy': 0.6401809677171996, 'f1': [0.6037145176043268, 0.5656119351444905, 0.6186873001937258, 0.660572334140312, 0.5566991823387576], 'avgF1': 0.6010570538843225, 'precision': [0.654320987654321, 0.6296296296296297, 0.6666666666666666, 0.7037037037037037, 0.546583850931677], 'avgPrecision': 0.6401809677171996, 'recall': [0.654320987654321, 0.6296296296296297, 0.6666666666666666, 0.7037037037037037, 0.546583850931677], 'avgRecall': 0.6401809677171996, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T02:53:43.069559 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.6604938271604939, 0.6111111111111112, 0.6851851851851852, 0.6604938271604939, 0.6149068322981367], 'avgAccuracy': 0.6464381565830841, 'f1': [0.6381940368302812, 0.5995990154425831, 0.6523195684165289, 0.6506201710776066, 0.6092434025270627], 'avgF1': 0.6299952388588125, 'precision': [0.6604938271604939, 0.6111111111111112, 0.6851851851851852, 0.6604938271604939, 0.6149068322981367], 'avgPrecision': 0.6464381565830841, 'recall': [0.6604938271604939, 0.6111111111111112, 0.6851851851851852, 0.6604938271604939, 0.6149068322981367], 'avgRecall': 0.6464381565830841, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC
2021-05-29T02:53:45.234018 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.6419753086419753, 0.6358024691358025, 0.6790123456790124, 0.691358024691358, 0.6708074534161491], 'avgAccuracy': 0.6637911203128595, 'f1': [0.5586258799709618, 0.5587625610106229, 0.6001226483969817, 0.6224141309709637, 0.6218845216954857], 'avgF1': 0.5923619484090031, 'precision': [0.6419753086419753, 0.6358024691358025, 0.6790123456790124, 0.691358024691358, 0.6708074534161491], 'avgPrecision': 0.6637911203128595, 'recall': [0.6419753086419753, 0.6358024691358025, 0.6790123456790124, 0.691358024691358, 0.6708074534161491], 'avgRecall': 0.6637911203128595, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T02:54:22.665226 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.7037037037037037, 0.6894409937888198], 'avgAccuracy': 0.6712215320910974, 'f1': [0.6416205964915137, 0.5916420442674649, 0.65354622203565, 0.6659561830127132, 0.6786878001270805], 'avgF1': 0.6462905691868844, 'precision': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.7037037037037037, 0.6894409937888198], 'avgPrecision': 0.6712215320910974, 'recall': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.7037037037037037, 0.6894409937888198], 'avgRecall': 0.6712215320910974, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.6975308641975309, 0.6234567901234568, 0.7037037037037037, 0.7222222222222222, 0.639751552795031], 'avgAccuracy': 0.6773330266083889, 'f1': [0.6742774567099377, 0.6062065984546604, 0.6670215046629968, 0.7030467546493573, 0.6294074078460584], 'avgF1': 0.6559919444646021, 'precision': [0.6975308641975309, 0.6234567901234568, 0.7037037037037037, 0.7222222222222222, 0.639751552795031], 'avgPrecision': 0.6773330266083889, 'recall': [0.6975308641975309, 0.6234567901234568, 0.7037037037037037, 0.7222222222222222, 0.639751552795031], 'avgRecall': 0.6773330266083889, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.674849  0.651959   0.674849  0.674849   
1  0.655103  0.622627   0.655103  0.655103   
2  0.665033  0.638165   0.665033  0.665033   
3  0.603190  0.602798   0.603190  0.603190   
4  0.510567  0.508428   0.510567  0.510567   
5  0.629124  0.613825   0.629124  0.629124   
6  0.663791  0.592362   0.663791  0.663791   
7  0.669987  0.644357   0.669987  0.669987   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:01:11.541569 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6588451805843111, 'f1': [0.6220598745119486, 0.5914528337008957, 0.6523195684165289, 0.6519890260631002, 0.6599120785165806], 'avgF1': 0.6355466762418108, 'precision': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6588451805843111, 'recall': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6588451805843111, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T03:01:18.466245 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.6358024691358025, 0.5925925925925926, 0.6666666666666666, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6489686373744344, 'f1': [0.625888046851562, 0.566818407882499, 0.6384173647609738, 0.6444823944823945, 0.6583909094341079], 'avgF1': 0.6267994246823074, 'precision': [0.6358024691358025, 0.5925925925925926, 0.6666666666666666, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6489686373744344, 'recall': [0.6358024691358025, 0.5925925925925926, 0.6666666666666666, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6489686373744344, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T03:01:35.359273 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgAccuracy': 0.662564220535235, 'f1': [0.6220598745119486, 0.5813482097969886, 0.65354622203565, 0.6567057085575605, 0.6685069665212248], 'avgF1': 0.6364333962846745, 'precision': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgPrecision': 0.662564220535235, 'recall': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgRecall': 0.662564220535235, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T03:01:35.937396 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.6049382716049383, 0.5679012345679012, 0.6296296296296297, 0.6358024691358025, 0.5838509316770186], 'avgAccuracy': 0.6044245073230581, 'f1': [0.6106853352716236, 0.5570668212645697, 0.6234567901234568, 0.6368392145326249, 0.593124084025403], 'avgF1': 0.6042344490435356, 'precision': [0.6049382716049383, 0.5679012345679012, 0.6296296296296297, 0.6358024691358025, 0.5838509316770186], 'avgPrecision': 0.6044245073230581, 'recall': [0.6049382716049383, 0.5679012345679012, 0.6296296296296297, 0.6358024691358025, 0.5838509316770186], 'avgRecall': 0.6044245073230581, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T03:01:44.045626 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.6296296296296297, 0.5740740740740741, 0.691358024691358, 0.6728395061728395, 0.6708074534161491], 'avgAccuracy': 0.6477417375968101, 'f1': [0.6158427357153329, 0.5570805354232851, 0.6638536063360331, 0.636239970887414, 0.6330211201019699], 'avgF1': 0.621207593692807, 'precision': [0.6296296296296297, 0.5740740740740741, 0.691358024691358, 0.6728395061728395, 0.6708074534161491], 'avgPrecision': 0.6477417375968101, 'recall': [0.6296296296296297, 0.5740740740740741, 0.691358024691358, 0.6728395061728395, 0.6708074534161491], 'avgRecall': 0.6477417375968101, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T03:01:45.038043 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.6049382716049383, 0.6049382716049383, 0.6604938271604939, 0.654320987654321, 0.6149068322981367], 'avgAccuracy': 0.6279196380645656, 'f1': [0.5856692370236528, 0.6005561153475301, 0.627440549623831, 0.6429515775921004, 0.6224637186000767], 'avgF1': 0.6158162396374381, 'precision': [0.6049382716049383, 0.6049382716049383, 0.6604938271604939, 0.654320987654321, 0.6149068322981367], 'avgPrecision': 0.6279196380645656, 'recall': [0.6049382716049383, 0.6049382716049383, 0.6604938271604939, 0.654320987654321, 0.6149068322981367], 'avgRecall': 0.6279196380645656, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T03:01:47.203378 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6001226483969817, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5895198382375251, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T03:02:26.358845 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgAccuracy': 0.6699869641898627, 'f1': [0.6418471253812619, 0.5916420442674649, 0.65354622203565, 0.6614359237305492, 0.6786878001270805], 'avgF1': 0.6454318231084013, 'precision': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgPrecision': 0.6699869641898627, 'recall': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgRecall': 0.6699869641898627, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgAccuracy': 0.6699869641898627, 'f1': [0.6418471253812619, 0.5916420442674649, 0.65354622203565, 0.6614359237305492, 0.6786878001270805], 'avgF1': 0.6454318231084013, 'precision': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgPrecision': 0.6699869641898627, 'recall': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgRecall': 0.6699869641898627, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.651430  0.628582   0.651430  0.651430   
1  0.646492  0.627262   0.646492  0.646492   
2  0.662564  0.636433   0.662564  0.662564   
3  0.604425  0.604234   0.604425  0.604425   
4  0.561276  0.554512   0.561276  0.561276   
5  0.625443  0.611535   0.625443  0.625443   
6  0.660080  0.589520   0.660080  0.660080   
7  0.667510  0.641614   0.667510  0.667510   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:09:11.933702 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.654320987654321, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6459627329192547], 'avgAccuracy': 0.657587608312246, 'f1': [0.627269385017358, 0.5957480394648117, 0.6718269188558792, 0.6729897445946829, 0.6477390802774696], 'avgF1': 0.6431146336420402, 'precision': [0.654320987654321, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6459627329192547], 'avgPrecision': 0.657587608312246, 'recall': [0.654320987654321, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6459627329192547], 'avgRecall': 0.657587608312246, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T03:09:18.081612 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.6358024691358025, 0.6049382716049383, 0.6666666666666666, 0.6851851851851852, 0.6770186335403726], 'avgAccuracy': 0.653922245226593, 'f1': [0.625888046851562, 0.5816896460688556, 0.6384173647609738, 0.6366168810613256, 0.6747520668345018], 'avgF1': 0.6314728011154438, 'precision': [0.6358024691358025, 0.6049382716049383, 0.6666666666666666, 0.6851851851851852, 0.6770186335403726], 'avgPrecision': 0.653922245226593, 'recall': [0.6358024691358025, 0.6049382716049383, 0.6666666666666666, 0.6851851851851852, 0.6770186335403726], 'avgRecall': 0.653922245226593, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T03:09:32.253568 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6832298136645962], 'avgAccuracy': 0.6638064565600797, 'f1': [0.6220598745119486, 0.5813482097969886, 0.65354622203565, 0.6567057085575605, 0.6708316695923395], 'avgF1': 0.6368983368988974, 'precision': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6832298136645962], 'avgPrecision': 0.6638064565600797, 'recall': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6832298136645962], 'avgRecall': 0.6638064565600797, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T03:09:32.612909 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.5987654320987654, 0.5864197530864198, 0.6296296296296297, 0.6172839506172839, 0.577639751552795], 'avgAccuracy': 0.6019477033969788, 'f1': [0.6023155128419906, 0.5766398411125934, 0.6234567901234568, 0.6176832971128625, 0.5876954108309288], 'avgF1': 0.6015581704043664, 'precision': [0.5987654320987654, 0.5864197530864198, 0.6296296296296297, 0.6172839506172839, 0.577639751552795], 'avgPrecision': 0.6019477033969788, 'recall': [0.5987654320987654, 0.5864197530864198, 0.6296296296296297, 0.6172839506172839, 0.577639751552795], 'avgRecall': 0.6019477033969788, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T03:09:39.149325 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.6604938271604939, 0.6111111111111112, 0.6728395061728395, 0.6851851851851852, 0.6086956521739131], 'avgAccuracy': 0.6476650563607086, 'f1': [0.6429785548143035, 0.562470364585014, 0.6452686872706647, 0.6446818613485281, 0.6204832277738682], 'avgF1': 0.6231765391584757, 'precision': [0.6604938271604939, 0.6111111111111112, 0.6728395061728395, 0.6851851851851852, 0.6086956521739131], 'avgPrecision': 0.6476650563607086, 'recall': [0.6604938271604939, 0.6111111111111112, 0.6728395061728395, 0.6851851851851852, 0.6086956521739131], 'avgRecall': 0.6476650563607086, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T03:09:40.029414 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.6049382716049383, 0.5864197530864198, 0.6604938271604939, 0.691358024691358, 0.6335403726708074], 'avgAccuracy': 0.6353500498428035, 'f1': [0.5894170518631877, 0.5860132103260539, 0.6506184746364243, 0.6772100866199972, 0.6414359499095904], 'avgF1': 0.6289389546710507, 'precision': [0.6049382716049383, 0.5864197530864198, 0.6604938271604939, 0.691358024691358, 0.6335403726708074], 'avgPrecision': 0.6353500498428035, 'recall': [0.6049382716049383, 0.5864197530864198, 0.6604938271604939, 0.691358024691358, 0.6335403726708074], 'avgRecall': 0.6353500498428035, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T03:09:41.457362 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6001226483969817, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5895198382375251, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T03:10:17.974046 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgAccuracy': 0.6699869641898627, 'f1': [0.6418471253812619, 0.5916420442674649, 0.65354622203565, 0.6614359237305492, 0.6760578894226357], 'avgF1': 0.6449058409675124, 'precision': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgPrecision': 0.6699869641898627, 'recall': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgRecall': 0.6699869641898627, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgAccuracy': 0.6699869641898627, 'f1': [0.6418471253812619, 0.5916420442674649, 0.65354622203565, 0.6614359237305492, 0.6760578894226357], 'avgF1': 0.6449058409675124, 'precision': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgPrecision': 0.6699869641898627, 'recall': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgRecall': 0.6699869641898627, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.653876  0.634553   0.653876  0.653876   
1  0.645227  0.631349   0.645227  0.645227   
2  0.663806  0.636898   0.663806  0.663806   
3  0.601948  0.601558   0.601948  0.601948   
4  0.557565  0.558108   0.557565  0.557565   
5  0.610605  0.608085   0.610605  0.610605   
6  0.660080  0.589520   0.660080  0.660080   
7  0.669987  0.645432   0.669987  0.669987   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:17:05.270442 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.6358024691358025, 0.6049382716049383, 0.6851851851851852, 0.7098765432098766, 0.6770186335403726], 'avgAccuracy': 0.662564220535235, 'f1': [0.606709077752292, 0.5863375331589531, 0.6588785046728972, 0.6843764212503418, 0.6760109952425711], 'avgF1': 0.642462506415411, 'precision': [0.6358024691358025, 0.6049382716049383, 0.6851851851851852, 0.7098765432098766, 0.6770186335403726], 'avgPrecision': 0.662564220535235, 'recall': [0.6358024691358025, 0.6049382716049383, 0.6851851851851852, 0.7098765432098766, 0.6770186335403726], 'avgRecall': 0.662564220535235, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T03:17:11.336592 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.654320987654321, 0.6234567901234568, 0.6728395061728395, 0.691358024691358, 0.6708074534161491], 'avgAccuracy': 0.6625565524116249, 'f1': [0.627269385017358, 0.5909847832328452, 0.6432898948331047, 0.6409072183040061, 0.6604707455058395], 'avgF1': 0.6325844053786307, 'precision': [0.654320987654321, 0.6234567901234568, 0.6728395061728395, 0.691358024691358, 0.6708074534161491], 'avgPrecision': 0.6625565524116249, 'recall': [0.654320987654321, 0.6234567901234568, 0.6728395061728395, 0.691358024691358, 0.6708074534161491], 'avgRecall': 0.6625565524116249, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T03:17:25.283470 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6894409937888198], 'avgAccuracy': 0.6650486925849245, 'f1': [0.6220598745119486, 0.5813482097969886, 0.65354622203565, 0.6567057085575605, 0.6760578894226357], 'avgF1': 0.6379435808649567, 'precision': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6894409937888198], 'avgPrecision': 0.6650486925849245, 'recall': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6894409937888198], 'avgRecall': 0.6650486925849245, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-29T03:17:25.642845 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.5987654320987654, 0.5740740740740741, 0.6481481481481481, 0.6296296296296297, 0.6086956521739131], 'avgAccuracy': 0.611862587224906, 'f1': [0.6023155128419906, 0.5621240697878593, 0.6402425817630496, 0.6331430465525587, 0.6185176168914422], 'avgF1': 0.6112685655673801, 'precision': [0.5987654320987654, 0.5740740740740741, 0.6481481481481481, 0.6296296296296297, 0.6086956521739131], 'avgPrecision': 0.611862587224906, 'recall': [0.5987654320987654, 0.5740740740740741, 0.6481481481481481, 0.6296296296296297, 0.6086956521739131], 'avgRecall': 0.611862587224906, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-29T03:17:32.253236 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.6666666666666666, 0.5679012345679012, 0.6851851851851852, 0.6790123456790124, 0.6956521739130435], 'avgAccuracy': 0.6588835212023618, 'f1': [0.6525421020675551, 0.5591001998496505, 0.6530456650961214, 0.6407887674096675, 0.6760710318205834], 'avgF1': 0.6363095532487156, 'precision': [0.6666666666666666, 0.5679012345679012, 0.6851851851851852, 0.6790123456790124, 0.6956521739130435], 'avgPrecision': 0.6588835212023618, 'recall': [0.6666666666666666, 0.5679012345679012, 0.6851851851851852, 0.6790123456790124, 0.6956521739130435], 'avgRecall': 0.6588835212023618, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 100, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T03:17:33.108222 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.6296296296296297, 0.5987654320987654, 0.6481481481481481, 0.6419753086419753, 0.6459627329192547], 'avgAccuracy': 0.6328962502875546, 'f1': [0.6108389978121779, 0.5986168844649107, 0.6245372912397985, 0.6246112219197104, 0.6492346531901224], 'avgF1': 0.621567809725344, 'precision': [0.6296296296296297, 0.5987654320987654, 0.6481481481481481, 0.6419753086419753, 0.6459627329192547], 'avgPrecision': 0.6328962502875546, 'recall': [0.6296296296296297, 0.5987654320987654, 0.6481481481481481, 0.6419753086419753, 0.6459627329192547], 'avgRecall': 0.6328962502875546, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
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Processing Model: SVC
2021-05-29T03:17:34.594618 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6001226483969817, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5895198382375251, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T03:18:13.212497 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6956521739130435], 'avgAccuracy': 0.6712292002147074, 'f1': [0.6418471253812619, 0.5916420442674649, 0.65354622203565, 0.6614359237305492, 0.681323642706428], 'avgF1': 0.6459589916242708, 'precision': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6956521739130435], 'avgPrecision': 0.6712292002147074, 'recall': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6956521739130435], 'avgRecall': 0.6712292002147074, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6956521739130435], 'avgAccuracy': 0.6712292002147074, 'f1': [0.6418471253812619, 0.5916420442674649, 0.65354622203565, 0.6614359237305492, 0.681323642706428], 'avgF1': 0.6459589916242708, 'precision': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6956521739130435], 'avgPrecision': 0.6712292002147074, 'recall': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6956521739130435], 'avgRecall': 0.6712292002147074, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.655141  0.634238   0.655141  0.655141   
1  0.618104  0.589932   0.618104  0.618104   
2  0.665049  0.637944   0.665049  0.665049   
3  0.611863  0.611269   0.611863  0.611863   
4  0.636646  0.622382   0.636646  0.636646   
5  0.626700  0.615031   0.626700  0.626700   
6  0.660080  0.589520   0.660080  0.660080   
7  0.668752  0.643819   0.668752  0.668752   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:24:52.946876 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.6278531809932776, 0.5914528337008957, 0.6530456650961214, 0.6679011635381619, 0.6553240536045052], 'avgF1': 0.6391153793865924, 'precision': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6481481481481481, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T03:24:59.004365 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.6358024691358025, 0.6111111111111112, 0.691358024691358, 0.6975308641975309, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5808836402631875, 0.5813482097969886, 0.658384112492769, 0.6461066731219236, 0.6639030982847446], 'avgF1': 0.6261251467919227, 'precision': [0.6358024691358025, 0.6111111111111112, 0.691358024691358, 0.6975308641975309, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6111111111111112, 0.691358024691358, 0.6975308641975309, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T03:25:13.425913 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6894409937888198], 'avgAccuracy': 0.6675178283873936, 'f1': [0.6364135526199495, 0.5916420442674649, 0.65354622203565, 0.6567057085575605, 0.6786878001270805], 'avgF1': 0.6433990655215411, 'precision': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6894409937888198], 'avgPrecision': 0.6675178283873936, 'recall': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6894409937888198], 'avgRecall': 0.6675178283873936, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T03:25:13.769631 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.6172839506172839, 0.5802469135802469, 0.6419753086419753, 0.6604938271604939, 0.6211180124223602], 'avgAccuracy': 0.6242236024844721, 'f1': [0.6149403155980189, 0.5675423632954071, 0.6346691781793865, 0.6579802123636537, 0.6328312300949723], 'avgF1': 0.6215926599062876, 'precision': [0.6172839506172839, 0.5802469135802469, 0.6419753086419753, 0.6604938271604939, 0.6211180124223602], 'avgPrecision': 0.6242236024844721, 'recall': [0.6172839506172839, 0.5802469135802469, 0.6419753086419753, 0.6604938271604939, 0.6211180124223602], 'avgRecall': 0.6242236024844721, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T03:25:20.556218 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.6604938271604939, 0.6172839506172839, 0.6790123456790124, 0.691358024691358, 0.6770186335403726], 'avgAccuracy': 0.6650333563377041, 'f1': [0.6429785548143035, 0.5742884561203285, 0.642926116994092, 0.6417539673199425, 0.6626671891240248], 'avgF1': 0.6329228568745383, 'precision': [0.6604938271604939, 0.6172839506172839, 0.6790123456790124, 0.691358024691358, 0.6770186335403726], 'avgPrecision': 0.6650333563377041, 'recall': [0.6604938271604939, 0.6172839506172839, 0.6790123456790124, 0.691358024691358, 0.6770186335403726], 'avgRecall': 0.6650333563377041, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T03:25:21.601881 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.6481481481481481, 0.6111111111111112, 0.654320987654321, 0.6728395061728395, 0.6211180124223602], 'avgAccuracy': 0.641507553101756, 'f1': [0.6359396433470508, 0.5995990154425831, 0.6333955955100521, 0.6571150688820176, 0.6247595660459999], 'avgF1': 0.6301617778455407, 'precision': [0.6481481481481481, 0.6111111111111112, 0.654320987654321, 0.6728395061728395, 0.6211180124223602], 'avgPrecision': 0.641507553101756, 'recall': [0.6481481481481481, 0.6111111111111112, 0.654320987654321, 0.6728395061728395, 0.6211180124223602], 'avgRecall': 0.641507553101756, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T03:25:23.024011 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6001226483969817, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5895198382375251, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T03:25:55.247954 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgAccuracy': 0.6687523962886281, 'f1': [0.6364135526199495, 0.5916420442674649, 0.65354622203565, 0.6614359237305492, 0.6786878001270805], 'avgF1': 0.6443451085561388, 'precision': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgPrecision': 0.6687523962886281, 'recall': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgRecall': 0.6687523962886281, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgAccuracy': 0.6687523962886281, 'f1': [0.6364135526199495, 0.5916420442674649, 0.65354622203565, 0.6614359237305492, 0.6786878001270805], 'avgF1': 0.6443451085561388, 'precision': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgPrecision': 0.6687523962886281, 'recall': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgRecall': 0.6687523962886281, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.657603  0.637934   0.657603  0.657603   
1  0.620551  0.586551   0.620551  0.620551   
2  0.667518  0.643399   0.667518  0.667518   
3  0.624224  0.621593   0.624224  0.624224   
4  0.637888  0.625485   0.637888  0.637888   
5  0.639054  0.625823   0.639054  0.639054   
6  0.660080  0.589520   0.660080  0.660080   
7  0.668752  0.644486   0.668752  0.668752   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:29:17.950032 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.654320987654321, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgAccuracy': 0.6637987884364696, 'f1': [0.6331826776271221, 0.5866697596746655, 0.6530456650961214, 0.6561498713545497, 0.6656380479468935], 'avgF1': 0.6389372043398704, 'precision': [0.654320987654321, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgPrecision': 0.6637987884364696, 'recall': [0.654320987654321, 0.6111111111111112, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgRecall': 0.6637987884364696, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T03:29:23.264654 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.6419753086419753, 0.6172839506172839, 0.691358024691358, 0.691358024691358, 0.6894409937888198], 'avgAccuracy': 0.666283260486159, 'f1': [0.5671196801660119, 0.5916420442674649, 0.658384112492769, 0.6567057085575605, 0.6760578894226357], 'avgF1': 0.6299818869812884, 'precision': [0.6419753086419753, 0.6172839506172839, 0.691358024691358, 0.691358024691358, 0.6894409937888198], 'avgPrecision': 0.666283260486159, 'recall': [0.6419753086419753, 0.6172839506172839, 0.691358024691358, 0.691358024691358, 0.6894409937888198], 'avgRecall': 0.666283260486159, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T03:29:26.377413 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6894409937888198], 'avgAccuracy': 0.6675178283873936, 'f1': [0.6364135526199495, 0.5916420442674649, 0.65354622203565, 0.6567057085575605, 0.6786878001270805], 'avgF1': 0.6433990655215411, 'precision': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6894409937888198], 'avgPrecision': 0.6675178283873936, 'recall': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6894409937888198], 'avgRecall': 0.6675178283873936, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T03:29:26.706855 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.6234567901234568, 0.5679012345679012, 0.6419753086419753, 0.6666666666666666, 0.6273291925465838], 'avgAccuracy': 0.6254658385093167, 'f1': [0.6254968403096794, 0.5566785226241767, 0.6378600823045267, 0.6492393718843716, 0.6307597935580936], 'avgF1': 0.6200069221361696, 'precision': [0.6234567901234568, 0.5679012345679012, 0.6419753086419753, 0.6666666666666666, 0.6273291925465838], 'avgPrecision': 0.6254658385093167, 'recall': [0.6234567901234568, 0.5679012345679012, 0.6419753086419753, 0.6666666666666666, 0.6273291925465838], 'avgRecall': 0.6254658385093167, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T03:29:32.715849 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6851851851851852, 0.5341614906832298], 'avgAccuracy': 0.6364619277662756, 'f1': [0.6469965397168462, 0.5916420442674649, 0.65354622203565, 0.6522580260534254, 0.5507934472613317], 'avgF1': 0.6190472558669436, 'precision': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6851851851851852, 0.5341614906832298], 'avgPrecision': 0.6364619277662756, 'recall': [0.6604938271604939, 0.6172839506172839, 0.6851851851851852, 0.6851851851851852, 0.5341614906832298], 'avgRecall': 0.6364619277662756, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T03:29:33.450819 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.6172839506172839, 0.6111111111111112, 0.6728395061728395, 0.6728395061728395, 0.6708074534161491], 'avgAccuracy': 0.6489763054980446, 'f1': [0.601264002755796, 0.5866697596746655, 0.6432898948331047, 0.6418793147282307, 0.6604707455058395], 'avgF1': 0.6267147434995273, 'precision': [0.6172839506172839, 0.6111111111111112, 0.6728395061728395, 0.6728395061728395, 0.6708074534161491], 'avgPrecision': 0.6489763054980446, 'recall': [0.6172839506172839, 0.6111111111111112, 0.6728395061728395, 0.6728395061728395, 0.6708074534161491], 'avgRecall': 0.6489763054980446, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T03:29:34.763409 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6001226483969817, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5895198382375251, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T03:30:05.633962 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgAccuracy': 0.6687523962886281, 'f1': [0.6364135526199495, 0.5916420442674649, 0.65354622203565, 0.6614359237305492, 0.6786878001270805], 'avgF1': 0.6443451085561388, 'precision': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgPrecision': 0.6687523962886281, 'recall': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgRecall': 0.6687523962886281, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgAccuracy': 0.6687523962886281, 'f1': [0.6364135526199495, 0.5916420442674649, 0.65354622203565, 0.6614359237305492, 0.6786878001270805], 'avgF1': 0.6443451085561388, 'precision': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgPrecision': 0.6687523962886281, 'recall': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6975308641975309, 0.6894409937888198], 'avgRecall': 0.6687523962886281, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.661330  0.636786   0.661330  0.661330   
1  0.631700  0.595161   0.631700  0.631700   
2  0.667518  0.643399   0.667518  0.667518   
3  0.625466  0.620007   0.625466  0.625466   
4  0.629016  0.612307   0.629016  0.629016   
5  0.647742  0.625719   0.647742  0.647742   
6  0.660080  0.589520   0.660080  0.660080   
7  0.668752  0.644486   0.668752  0.668752   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:33:05.297869 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6481481481481481, 0.6172839506172839, 0.6851851851851852, 0.6851851851851852, 0.6770186335403726], 'avgAccuracy': 0.662564220535235, 'f1': [0.6278531809932776, 0.5916420442674649, 0.6530456650961214, 0.6517701533306176, 0.6657252766541621], 'avgF1': 0.6380072640683287, 'precision': [0.6481481481481481, 0.6172839506172839, 0.6851851851851852, 0.6851851851851852, 0.6770186335403726], 'avgPrecision': 0.662564220535235, 'recall': [0.6481481481481481, 0.6172839506172839, 0.6851851851851852, 0.6851851851851852, 0.6770186335403726], 'avgRecall': 0.662564220535235, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T03:33:10.573778 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6419753086419753, 0.6172839506172839, 0.691358024691358, 0.6851851851851852, 0.6770186335403726], 'avgAccuracy': 0.662564220535235, 'f1': [0.5680757496203289, 0.5916420442674649, 0.658384112492769, 0.6522580260534254, 0.6657252766541621], 'avgF1': 0.62721704181763, 'precision': [0.6419753086419753, 0.6172839506172839, 0.691358024691358, 0.6851851851851852, 0.6770186335403726], 'avgPrecision': 0.662564220535235, 'recall': [0.6419753086419753, 0.6172839506172839, 0.691358024691358, 0.6851851851851852, 0.6770186335403726], 'avgRecall': 0.662564220535235, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T03:33:13.689606 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6851851851851852, 0.6770186335403726], 'avgAccuracy': 0.6637987884364696, 'f1': [0.637536971349645, 0.5916420442674649, 0.65354622203565, 0.6522580260534254, 0.6683285552113006], 'avgF1': 0.6406623637834972, 'precision': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6851851851851852, 0.6770186335403726], 'avgPrecision': 0.6637987884364696, 'recall': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.6851851851851852, 0.6770186335403726], 'avgRecall': 0.6637987884364696, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-29T03:33:14.033371 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6358024691358025, 0.5987654320987654, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgAccuracy': 0.6390614216701174, 'f1': [0.6279565287848432, 0.5777481747637021, 0.6376559870711918, 0.6450083556026708, 0.6414490613790377], 'avgF1': 0.6259636215202891, 'precision': [0.6358024691358025, 0.5987654320987654, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgPrecision': 0.6390614216701174, 'recall': [0.6358024691358025, 0.5987654320987654, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgRecall': 0.6390614216701174, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-29T03:33:20.455888 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.654320987654321, 0.6111111111111112, 0.6975308641975309, 0.6790123456790124, 0.5031055900621118], 'avgAccuracy': 0.6290161797408175, 'f1': [0.637536971349645, 0.5866697596746655, 0.6688400306544482, 0.6469561174422286, 0.5210211151409533], 'avgF1': 0.6122047988523881, 'precision': [0.654320987654321, 0.6111111111111112, 0.6975308641975309, 0.6790123456790124, 0.5031055900621118], 'avgPrecision': 0.6290161797408175, 'recall': [0.654320987654321, 0.6111111111111112, 0.6975308641975309, 0.6790123456790124, 0.5031055900621118], 'avgRecall': 0.6290161797408175, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T03:33:21.463157 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6419753086419753, 0.6172839506172839, 0.6790123456790124, 0.6790123456790124, 0.6770186335403726], 'avgAccuracy': 0.6588605168315314, 'f1': [0.6236772591777753, 0.5916420442674649, 0.6481647127875735, 0.6469561174422286, 0.6657252766541621], 'avgF1': 0.6352330820658408, 'precision': [0.6419753086419753, 0.6172839506172839, 0.6790123456790124, 0.6790123456790124, 0.6770186335403726], 'avgPrecision': 0.6588605168315314, 'recall': [0.6419753086419753, 0.6172839506172839, 0.6790123456790124, 0.6790123456790124, 0.6770186335403726], 'avgRecall': 0.6588605168315314, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-29T03:33:22.711022 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6001226483969817, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5895198382375251, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T03:33:54.238032 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgAccuracy': 0.6650333563377041, 'f1': [0.637536971349645, 0.5916420442674649, 0.65354622203565, 0.6570330818995961, 0.6683285552113006], 'avgF1': 0.6416173749527313, 'precision': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgPrecision': 0.6650333563377041, 'recall': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgRecall': 0.6650333563377041, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgAccuracy': 0.6650333563377041, 'f1': [0.637536971349645, 0.5916420442674649, 0.65354622203565, 0.6570330818995961, 0.6683285552113006], 'avgF1': 0.6416173749527313, 'precision': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgPrecision': 0.6650333563377041, 'recall': [0.654320987654321, 0.6172839506172839, 0.6851851851851852, 0.691358024691358, 0.6770186335403726], 'avgRecall': 0.6650333563377041, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.658861  0.635233   0.658861  0.658861   
1  0.656391  0.623785   0.656391  0.656391   
2  0.663799  0.640662   0.663799  0.663799   
3  0.639061  0.625964   0.639061  0.639061   
4  0.626547  0.609942   0.626547  0.626547   
5  0.657618  0.634172   0.657618  0.657618   
6  0.660080  0.589520   0.660080  0.660080   
7  0.663799  0.640662   0.663799  0.663799   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:36:51.963566 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.6296296296296297, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6588451805843111, 'f1': [0.5501995574580724, 0.5587625610106229, 0.5995242786520045, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5886828741340535, 'precision': [0.6296296296296297, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6588451805843111, 'recall': [0.6296296296296297, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6588451805843111, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-29T03:36:57.176052 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6728395061728395, 0.6708074534161491], 'avgAccuracy': 0.6526800092017484, 'f1': [0.5537860082304527, 0.5816896460688556, 0.6551934667725667, 0.6484787823151803, 0.6604197656734612], 'avgF1': 0.6199135338121033, 'precision': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6728395061728395, 0.6708074534161491], 'avgPrecision': 0.6526800092017484, 'recall': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6728395061728395, 0.6708074534161491], 'avgRecall': 0.6526800092017484, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T03:37:00.215817 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgAccuracy': 0.6576182808066866, 'f1': [0.6414635546784833, 0.5816896460688556, 0.6551934667725667, 0.6534067320744158, 0.6604197656734612], 'avgF1': 0.6384346330535565, 'precision': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgPrecision': 0.6576182808066866, 'recall': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgRecall': 0.6576182808066866, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-29T03:37:00.559561 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.6296296296296297, 0.5987654320987654, 0.654320987654321, 0.6666666666666666, 0.6149068322981367], 'avgAccuracy': 0.6328579096695038, 'f1': [0.6199224869394979, 0.5777481747637021, 0.6373340834516054, 0.6446687468909691, 0.6164197035134888], 'avgF1': 0.6192186391118527, 'precision': [0.6296296296296297, 0.5987654320987654, 0.654320987654321, 0.6666666666666666, 0.6149068322981367], 'avgPrecision': 0.6328579096695038, 'recall': [0.6296296296296297, 0.5987654320987654, 0.654320987654321, 0.6666666666666666, 0.6149068322981367], 'avgRecall': 0.6328579096695038, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T03:37:06.661240 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.6481481481481481, 0.5925925925925926, 0.6790123456790124, 0.6790123456790124, 0.4968944099378882], 'avgAccuracy': 0.6191319684073308, 'f1': [0.6321059386193487, 0.5716955123781968, 0.6551934667725667, 0.6534067320744158, 0.5113999892269383], 'avgF1': 0.6047603278142932, 'precision': [0.6481481481481481, 0.5925925925925926, 0.6790123456790124, 0.6790123456790124, 0.4968944099378882], 'avgPrecision': 0.6191319684073308, 'recall': [0.6481481481481481, 0.5925925925925926, 0.6790123456790124, 0.6790123456790124, 0.4968944099378882], 'avgRecall': 0.6191319684073308, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T03:37:07.379938 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.6296296296296297, 0.6049382716049383, 0.6728395061728395, 0.6790123456790124, 0.6645962732919255], 'avgAccuracy': 0.6502032052756691, 'f1': [0.5501995574580724, 0.5816896460688556, 0.5960928829554154, 0.6144632313472894, 0.616860233879694], 'avgF1': 0.5918611103418654, 'precision': [0.6296296296296297, 0.6049382716049383, 0.6728395061728395, 0.6790123456790124, 0.6645962732919255], 'avgPrecision': 0.6502032052756691, 'recall': [0.6296296296296297, 0.6049382716049383, 0.6728395061728395, 0.6790123456790124, 0.6645962732919255], 'avgRecall': 0.6502032052756691, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T03:37:08.581999 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6001226483969817, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5895198382375251, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-29T03:37:40.902574 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.6481481481481481, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgAccuracy': 0.6625565524116249, 'f1': [0.6321059386193487, 0.5587625610106229, 0.6551934667725667, 0.6534067320744158, 0.6604197656734612], 'avgF1': 0.6319776928300831, 'precision': [0.6481481481481481, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgPrecision': 0.6625565524116249, 'recall': [0.6481481481481481, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgRecall': 0.6625565524116249, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.6481481481481481, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgAccuracy': 0.6625565524116249, 'f1': [0.6321059386193487, 0.5587625610106229, 0.6551934667725667, 0.6534067320744158, 0.6604197656734612], 'avgF1': 0.6319776928300831, 'precision': [0.6481481481481481, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgPrecision': 0.6625565524116249, 'recall': [0.6481481481481481, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgRecall': 0.6625565524116249, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.652672  0.593299   0.652672  0.652672   
1  0.639100  0.610503   0.639100  0.639100   
2  0.657618  0.638435   0.657618  0.657618   
3  0.632858  0.619219   0.632858  0.632858   
4  0.619132  0.605554   0.619132  0.619132   
5  0.650203  0.591861   0.650203  0.650203   
6  0.660080  0.589520   0.660080  0.660080   
7  0.655149  0.613831   0.655149  0.655149   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:40:39.453016 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6539069089793728, 'f1': [0.5537860082304527, 0.5816896460688556, 0.5995242786520045, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5939855813001762, 'precision': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6539069089793728, 'recall': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6539069089793728, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T03:40:44.614997 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6728395061728395, 0.6708074534161491], 'avgAccuracy': 0.6526800092017484, 'f1': [0.5537860082304527, 0.5816896460688556, 0.6551934667725667, 0.6484787823151803, 0.6604197656734612], 'avgF1': 0.6199135338121033, 'precision': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6728395061728395, 0.6708074534161491], 'avgPrecision': 0.6526800092017484, 'recall': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6728395061728395, 0.6708074534161491], 'avgRecall': 0.6526800092017484, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T03:40:47.686414 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgAccuracy': 0.6576182808066866, 'f1': [0.6414635546784833, 0.5816896460688556, 0.6551934667725667, 0.6534067320744158, 0.6604197656734612], 'avgF1': 0.6384346330535565, 'precision': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgPrecision': 0.6576182808066866, 'recall': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgRecall': 0.6576182808066866, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T03:40:48.623860 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.6172839506172839, 0.5740740740740741, 0.6604938271604939, 0.6481481481481481, 0.6086956521739131], 'avgAccuracy': 0.6217391304347826, 'f1': [0.5427342983147387, 0.5582499971163939, 0.5973420129098672, 0.6035396894220424, 0.5813743845780459], 'avgF1': 0.5766480764682176, 'precision': [0.6172839506172839, 0.5740740740740741, 0.6604938271604939, 0.6481481481481481, 0.6086956521739131], 'avgPrecision': 0.6217391304347826, 'recall': [0.6172839506172839, 0.5740740740740741, 0.6604938271604939, 0.6481481481481481, 0.6086956521739131], 'avgRecall': 0.6217391304347826, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T03:40:54.899388 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.6481481481481481, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.4968944099378882], 'avgAccuracy': 0.6216011042097999, 'f1': [0.6321059386193487, 0.5816896460688556, 0.6551934667725667, 0.6534067320744158, 0.5113999892269383], 'avgF1': 0.606759154552425, 'precision': [0.6481481481481481, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.4968944099378882], 'avgPrecision': 0.6216011042097999, 'recall': [0.6481481481481481, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.4968944099378882], 'avgRecall': 0.6216011042097999, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T03:40:55.852448 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.6296296296296297, 0.6049382716049383, 0.6728395061728395, 0.6790123456790124, 0.6645962732919255], 'avgAccuracy': 0.6502032052756691, 'f1': [0.5501995574580724, 0.5816896460688556, 0.5966849565614997, 0.6144632313472894, 0.616860233879694], 'avgF1': 0.5919795250630823, 'precision': [0.6296296296296297, 0.6049382716049383, 0.6728395061728395, 0.6790123456790124, 0.6645962732919255], 'avgPrecision': 0.6502032052756691, 'recall': [0.6296296296296297, 0.6049382716049383, 0.6728395061728395, 0.6790123456790124, 0.6645962732919255], 'avgRecall': 0.6502032052756691, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T03:40:57.065878 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6001226483969817, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5895198382375251, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T03:41:34.835138 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6708074534161491], 'avgAccuracy': 0.6588528487079212, 'f1': [0.6414635546784833, 0.5816896460688556, 0.6551934667725667, 0.6180677396698739, 0.6604197656734612], 'avgF1': 0.6313668345726482, 'precision': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6708074534161491], 'avgPrecision': 0.6588528487079212, 'recall': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6708074534161491], 'avgRecall': 0.6588528487079212, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6001226483969817, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5895198382375251, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.650203  0.591861   0.650203  0.650203   
1  0.642803  0.627058   0.642803  0.642803   
2  0.657618  0.638435   0.657618  0.657618   
3  0.621739  0.576648   0.621739  0.621739   
4  0.617897  0.603682   0.617897  0.617897   
5  0.650203  0.591980   0.650203  0.650203   
6  0.660080  0.589520   0.660080  0.660080   
7  0.655149  0.612623   0.655149  0.655149   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:44:34.324783 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6539069089793728, 'f1': [0.5537860082304527, 0.5816896460688556, 0.5995242786520045, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5939855813001762, 'precision': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6539069089793728, 'recall': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6539069089793728, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T03:44:39.505747 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6708074534161491], 'avgAccuracy': 0.6588528487079212, 'f1': [0.6414635546784833, 0.5816896460688556, 0.6001226483969817, 0.6180677396698739, 0.6604197656734612], 'avgF1': 0.6203526708975311, 'precision': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6708074534161491], 'avgPrecision': 0.6588528487079212, 'recall': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6708074534161491], 'avgRecall': 0.6588528487079212, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T03:44:42.561396 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgAccuracy': 0.6576182808066866, 'f1': [0.6414635546784833, 0.5816896460688556, 0.6551934667725667, 0.6534067320744158, 0.6604197656734612], 'avgF1': 0.6384346330535565, 'precision': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgPrecision': 0.6576182808066866, 'recall': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgRecall': 0.6576182808066866, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-29T03:44:42.936342 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.6172839506172839, 0.6296296296296297, 0.6604938271604939, 0.6728395061728395, 0.6459627329192547], 'avgAccuracy': 0.6452419292999003, 'f1': [0.5427342983147387, 0.5565999544138609, 0.5973420129098672, 0.6100850764408623, 0.6063525353676196], 'avgF1': 0.5826227754893898, 'precision': [0.6172839506172839, 0.6296296296296297, 0.6604938271604939, 0.6728395061728395, 0.6459627329192547], 'avgPrecision': 0.6452419292999003, 'recall': [0.6172839506172839, 0.6296296296296297, 0.6604938271604939, 0.6728395061728395, 0.6459627329192547], 'avgRecall': 0.6452419292999003, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-29T03:44:49.145455 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgAccuracy': 0.6576182808066866, 'f1': [0.6414635546784833, 0.5816896460688556, 0.6551934667725667, 0.6534067320744158, 0.6604197656734612], 'avgF1': 0.6384346330535565, 'precision': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgPrecision': 0.6576182808066866, 'recall': [0.654320987654321, 0.6049382716049383, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgRecall': 0.6576182808066866, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-29T03:44:49.864200 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6539069089793728, 'f1': [0.5537860082304527, 0.5816896460688556, 0.5995242786520045, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5939855813001762, 'precision': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6539069089793728, 'recall': [0.6358024691358025, 0.6049382716049383, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6539069089793728, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-29T03:44:51.261454 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6001226483969817, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5895198382375251, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-29T03:45:28.707931 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgAccuracy': 0.6600874166091558, 'f1': [0.5537860082304527, 0.5587625610106229, 0.5995242786520045, 0.6534067320744158, 0.6604197656734612], 'avgF1': 0.6051798691281914, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgPrecision': 0.6600874166091558, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgRecall': 0.6600874166091558, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgAccuracy': 0.6600874166091558, 'f1': [0.5537860082304527, 0.5587625610106229, 0.5995242786520045, 0.6534067320744158, 0.6604197656734612], 'avgF1': 0.6051798691281914, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgPrecision': 0.6600874166091558, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6790123456790124, 0.6708074534161491], 'avgRecall': 0.6600874166091558, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
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                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.653907  0.593986   0.653907  0.653907   
1  0.650211  0.599573   0.650211  0.650211   
2  0.657618  0.638435   0.657618  0.657618   
3  0.645242  0.582623   0.645242  0.645242   
4  0.657618  0.638435   0.657618  0.657618   
5  0.653907  0.593986   0.653907  0.653907   
6  0.660080  0.589520   0.660080  0.660080   
7  0.660087  0.605180   0.660087  0.660087   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:48:26.136441 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.5995242786520045, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5894001642885296, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
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Processing Model: KNeighborsClassifier
2021-05-29T03:48:31.472529 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6459627329192547], 'avgAccuracy': 0.6563530404110115, 'f1': [0.5537860082304527, 0.5587625610106229, 0.5995242786520045, 0.6180677396698739, 0.6475563050690416], 'avgF1': 0.5955393785263992, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6459627329192547], 'avgPrecision': 0.6563530404110115, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6459627329192547], 'avgRecall': 0.6563530404110115, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 17, 'p': 2, 'weights': 'uniform'}]}
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Processing Model: LogisticRegression
2021-05-29T03:48:34.566662 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.5995242786520045, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5894001642885296, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
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Processing Model: GaussianNB
2021-05-29T03:48:34.926040 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6851851851851852, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6613143163867802, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6138251128321103, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5922603311245508, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6851851851851852, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6613143163867802, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6851851851851852, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6613143163867802, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
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Processing Model: AdaBoostClassifier
2021-05-29T03:48:41.096353 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.5995242786520045, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5894001642885296, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
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Processing Model: DecisionTreeClassifier
2021-05-29T03:48:42.452543 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.5995242786520045, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5894001642885296, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
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Processing Model: SVC
2021-05-29T03:48:43.596274 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6001226483969817, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5895198382375251, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
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Processing Model: MLPClassifier
2021-05-29T03:49:21.124108 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6600797484855456, 'f1': [0.5537860082304527, 0.5587625610106229, 0.5995242786520045, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5894001642885296, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6600797484855456, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6790123456790124, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6600797484855456, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
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*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.6358024691358025, 0.6358024691358025, 0.6851851851851852, 0.6851851851851852, 0.6645962732919255], 'avgAccuracy': 0.6613143163867802, 'f1': [0.5537860082304527, 0.5587625610106229, 0.6138251128321103, 0.6180677396698739, 0.616860233879694], 'avgF1': 0.5922603311245508, 'precision': [0.6358024691358025, 0.6358024691358025, 0.6851851851851852, 0.6851851851851852, 0.6645962732919255], 'avgPrecision': 0.6613143163867802, 'recall': [0.6358024691358025, 0.6358024691358025, 0.6851851851851852, 0.6851851851851852, 0.6645962732919255], 'avgRecall': 0.6613143163867802, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.660080  0.589400   0.660080  0.660080   
1  0.656353  0.595539   0.656353  0.656353   
2  0.660080  0.589400   0.660080  0.660080   
3  0.661314  0.592260   0.661314  0.661314   
4  0.660080  0.589400   0.660080  0.660080   
5  0.660080  0.589400   0.660080  0.660080   
6  0.660080  0.589520   0.660080  0.660080   
7  0.660080  0.589400   0.660080  0.660080   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:52:14.230853 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgAccuracy': 0.6415305574725865, 'f1': [0.5427342983147387, 0.5565999544138609, 0.5828997041118252, 0.6049325418652977, 0.6012422360248447], 'avgF1': 0.5776817469461134, 'precision': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgPrecision': 0.6415305574725865, 'recall': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgRecall': 0.6415305574725865, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T03:52:19.531412 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.6211180124223602], 'avgAccuracy': 0.6378038493980522, 'f1': [0.5427342983147387, 0.5565999544138609, 0.5828997041118252, 0.6049325418652977, 0.6294780451595303], 'avgF1': 0.5833289087730505, 'precision': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.6211180124223602], 'avgPrecision': 0.6378038493980522, 'recall': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.6211180124223602], 'avgRecall': 0.6378038493980522, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 17, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T03:52:22.504302 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgAccuracy': 0.6415305574725865, 'f1': [0.5427342983147387, 0.5565999544138609, 0.5828997041118252, 0.6049325418652977, 0.6012422360248447], 'avgF1': 0.5776817469461134, 'precision': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgPrecision': 0.6415305574725865, 'recall': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgRecall': 0.6415305574725865, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T03:52:22.941803 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgAccuracy': 0.6415305574725865, 'f1': [0.5427342983147387, 0.5565999544138609, 0.5828997041118252, 0.6049325418652977, 0.6012422360248447], 'avgF1': 0.5776817469461134, 'precision': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgPrecision': 0.6415305574725865, 'recall': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgRecall': 0.6415305574725865, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T03:52:29.271663 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgAccuracy': 0.6415305574725865, 'f1': [0.5427342983147387, 0.5565999544138609, 0.5828997041118252, 0.6049325418652977, 0.6012422360248447], 'avgF1': 0.5776817469461134, 'precision': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgPrecision': 0.6415305574725865, 'recall': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgRecall': 0.6415305574725865, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T03:52:30.053137 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgAccuracy': 0.6415305574725865, 'f1': [0.5427342983147387, 0.5565999544138609, 0.5828997041118252, 0.6049325418652977, 0.6012422360248447], 'avgF1': 0.5776817469461134, 'precision': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgPrecision': 0.6415305574725865, 'recall': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgRecall': 0.6415305574725865, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T03:52:31.328170 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgAccuracy': 0.6415305574725865, 'f1': [0.5427342983147387, 0.5565999544138609, 0.5828997041118252, 0.6049325418652977, 0.6012422360248447], 'avgF1': 0.5776817469461134, 'precision': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgPrecision': 0.6415305574725865, 'recall': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgRecall': 0.6415305574725865, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T03:53:11.780682 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgAccuracy': 0.6415305574725865, 'f1': [0.5427342983147387, 0.5565999544138609, 0.5828997041118252, 0.6049325418652977, 0.6012422360248447], 'avgF1': 0.5776817469461134, 'precision': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgPrecision': 0.6415305574725865, 'recall': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgRecall': 0.6415305574725865, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgAccuracy': 0.6415305574725865, 'f1': [0.5427342983147387, 0.5565999544138609, 0.5828997041118252, 0.6049325418652977, 0.6012422360248447], 'avgF1': 0.5776817469461134, 'precision': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgPrecision': 0.6415305574725865, 'recall': [0.6172839506172839, 0.6296296296296297, 0.654320987654321, 0.6666666666666666, 0.639751552795031], 'avgRecall': 0.6415305574725865, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                      features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch   
6                     SVC  Active inflammation?, Severity of Crypt Arch   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch   

   accuracy        f1  precision    recall  \
0  0.641531  0.577682   0.641531  0.641531   
1  0.637804  0.583329   0.637804  0.637804   
2  0.641531  0.577682   0.641531  0.641531   
3  0.641531  0.577682   0.641531  0.641531   
4  0.641531  0.577682   0.641531  0.641531   
5  0.641531  0.577682   0.641531  0.641531   
6  0.641531  0.577682   0.641531  0.641531   
7  0.641531  0.577682   0.641531  0.641531   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier
2021-05-29T03:55:59.358790 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?', 'accuracy': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgAccuracy': 0.5847481021394065, 'f1': [0.42611882716049393, 0.42611882716049393, 0.3541102077687443, 0.4484961151627818, 0.5070198054611509], 'avgF1': 0.432372756542733, 'precision': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgPrecision': 0.5847481021394065, 'recall': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgRecall': 0.5847481021394065, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier
2021-05-29T03:56:04.563685 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?', 'accuracy': [0.5185185185185185, 0.5432098765432098, 0.5925925925925926, 0.5617283950617284, 0.5279503105590062], 'avgAccuracy': 0.5487999386550111, 'f1': [0.46569973862147257, 0.48879520074950505, 0.5292718976929504, 0.5161573230789988, 0.5044741551742288], 'avgF1': 0.5008796630634311, 'precision': [0.5185185185185185, 0.5432098765432098, 0.5925925925925926, 0.5617283950617284, 0.5279503105590062], 'avgPrecision': 0.5487999386550111, 'recall': [0.5185185185185185, 0.5432098765432098, 0.5925925925925926, 0.5617283950617284, 0.5279503105590062], 'avgRecall': 0.5487999386550111, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression
2021-05-29T03:56:07.257324 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?', 'accuracy': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgAccuracy': 0.5847481021394065, 'f1': [0.42611882716049393, 0.42611882716049393, 0.3541102077687443, 0.4484961151627818, 0.5070198054611509], 'avgF1': 0.432372756542733, 'precision': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgPrecision': 0.5847481021394065, 'recall': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgRecall': 0.5847481021394065, 'params': [{'C': 0.0001, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB
2021-05-29T03:56:07.726080 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?', 'accuracy': [0.5185185185185185, 0.5432098765432098, 0.5925925925925926, 0.5617283950617284, 0.5279503105590062], 'avgAccuracy': 0.5487999386550111, 'f1': [0.46569973862147257, 0.48879520074950505, 0.5292718976929504, 0.5161573230789988, 0.5044741551742288], 'avgF1': 0.5008796630634311, 'precision': [0.5185185185185185, 0.5432098765432098, 0.5925925925925926, 0.5617283950617284, 0.5279503105590062], 'avgPrecision': 0.5487999386550111, 'recall': [0.5185185185185185, 0.5432098765432098, 0.5925925925925926, 0.5617283950617284, 0.5279503105590062], 'avgRecall': 0.5487999386550111, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier
2021-05-29T03:56:14.314797 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?', 'accuracy': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgAccuracy': 0.5847481021394065, 'f1': [0.42611882716049393, 0.42611882716049393, 0.3541102077687443, 0.4484961151627818, 0.5070198054611509], 'avgF1': 0.432372756542733, 'precision': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgPrecision': 0.5847481021394065, 'recall': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgRecall': 0.5847481021394065, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier
2021-05-29T03:56:14.986635 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?', 'accuracy': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgAccuracy': 0.5847481021394065, 'f1': [0.42611882716049393, 0.42611882716049393, 0.3541102077687443, 0.4484961151627818, 0.5070198054611509], 'avgF1': 0.432372756542733, 'precision': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgPrecision': 0.5847481021394065, 'recall': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgRecall': 0.5847481021394065, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC
2021-05-29T03:56:16.756463 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?', 'accuracy': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgAccuracy': 0.5847481021394065, 'f1': [0.42611882716049393, 0.42611882716049393, 0.3541102077687443, 0.4484961151627818, 0.5070198054611509], 'avgF1': 0.432372756542733, 'precision': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgPrecision': 0.5847481021394065, 'recall': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgRecall': 0.5847481021394065, 'params': [{'C': 0.0001, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier
2021-05-29T03:57:16.975595 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/ 
*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?', 'accuracy': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgAccuracy': 0.5847481021394065, 'f1': [0.42611882716049393, 0.42611882716049393, 0.3541102077687443, 0.4484961151627818, 0.5070198054611509], 'avgF1': 0.432372756542733, 'precision': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgPrecision': 0.5847481021394065, 'recall': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgRecall': 0.5847481021394065, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?', 'accuracy': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgAccuracy': 0.5847481021394065, 'f1': [0.42611882716049393, 0.42611882716049393, 0.3541102077687443, 0.4484961151627818, 0.5070198054611509], 'avgF1': 0.432372756542733, 'precision': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgPrecision': 0.5847481021394065, 'recall': [0.5802469135802469, 0.5802469135802469, 0.5185185185185185, 0.5987654320987654, 0.6459627329192547], 'avgRecall': 0.5847481021394065, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model              features  accuracy        f1  \
0  RandomForestClassifier  Active inflammation?  0.584748  0.432373   
1    KNeighborsClassifier  Active inflammation?  0.548800  0.500880   
2      LogisticRegression  Active inflammation?  0.584748  0.432373   
3              GaussianNB  Active inflammation?  0.548800  0.500880   
4      AdaBoostClassifier  Active inflammation?  0.584748  0.432373   
5  DecisionTreeClassifier  Active inflammation?  0.584748  0.432373   
6                     SVC  Active inflammation?  0.584748  0.432373   
7           MLPClassifier  Active inflammation?  0.561146  0.431864   

   precision    recall                                             params  
0   0.584748  0.584748  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1   0.548800  0.548800  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2   0.584748  0.584748  {'C': 1, 'class_weight': None, 'dual': False, ...  
3   0.548800  0.548800           {'priors': None, 'var_smoothing': 1e-09}  
4   0.584748  0.584748  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5   0.584748  0.584748  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6   0.584748  0.584748  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7   0.561146  0.561146  {'activation': 'logistic', 'alpha': 0.0001, 'b...
In [147]:
n_folds = 5
l = len(df_features['Features']) - 1
df = df_features['Features']
In [149]:
# MSMOTE Dataset
X3 = pd.concat([X_msm, X_test_ord]) #.to_numpy()
y3 = pd.concat([y_msm, y_test_ord]).to_numpy()
#data = (X, y, n_folds)

print('********************************************')
print('Starting MSMOTE data set....')
print('********************************************')

for i in range(l ,0, -1):
    col = []
    col = df[:i]
    nX3 = X3.loc[:, col]
    nX3 = nX3.to_numpy()
    data3 = (nX3, y3, n_folds)
    hyper_search(modelDictionary, modelParamsDictionary, data3, col)

********************************************
Starting MSMOTE data set....
********************************************

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.808, 0.852, 0.928, 0.82, 0.8072289156626506], 'avgAccuracy': 0.8430457831325301, 'f1': [0.8091223188405796, 0.851848501046171, 0.9299194359576968, 0.8771341365461847, 0.8061349340012379], 'avgF1': 0.854831865278374, 'precision': [0.808, 0.852, 0.928, 0.82, 0.8072289156626506], 'avgPrecision': 0.8430457831325301, 'recall': [0.808, 0.852, 0.928, 0.82, 0.8072289156626506], 'avgRecall': 0.8430457831325301, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.68, 0.764, 0.876, 0.78, 0.6867469879518072], 'avgAccuracy': 0.7573493975903615, 'f1': [0.6845335283613255, 0.771835076923077, 0.883936416137452, 0.8477310924369748, 0.688002350652953], 'avgF1': 0.7752076929023565, 'precision': [0.68, 0.764, 0.876, 0.78, 0.6867469879518072], 'avgPrecision': 0.7573493975903615, 'recall': [0.68, 0.764, 0.876, 0.78, 0.6867469879518072], 'avgRecall': 0.7573493975903615, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.64, 0.708, 0.856, 0.568, 0.6947791164658634], 'avgAccuracy': 0.6933558232931727, 'f1': [0.6449769289382443, 0.718490493566353, 0.8702875384686052, 0.6414906832298137, 0.6949614071072103], 'avgF1': 0.7140414102620453, 'precision': [0.64, 0.708, 0.856, 0.568, 0.6947791164658634], 'avgPrecision': 0.6933558232931727, 'recall': [0.64, 0.708, 0.856, 0.568, 0.6947791164658634], 'avgRecall': 0.6933558232931727, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgAccuracy': 0.634059437751004, 'f1': [0.5707754304175356, 0.6294854876782174, 0.874543583107165, 0.40804096073131607, 0.5401855099134929], 'avgF1': 0.6046061943695454, 'precision': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgPrecision': 0.634059437751004, 'recall': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgRecall': 0.634059437751004, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.74, 0.676, 0.864, 0.744, 0.6184738955823293], 'avgAccuracy': 0.7284947791164659, 'f1': [0.7459059379576622, 0.6955833781414377, 0.876150606198625, 0.8293073136427567, 0.6160273131803315], 'avgF1': 0.7525949098241627, 'precision': [0.74, 0.676, 0.864, 0.744, 0.6184738955823293], 'avgPrecision': 0.7284947791164659, 'recall': [0.74, 0.676, 0.864, 0.744, 0.6184738955823293], 'avgRecall': 0.7284947791164659, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.752, 0.7, 0.856, 0.808, 0.6987951807228916], 'avgAccuracy': 0.7629590361445783, 'f1': [0.7566241961527487, 0.7127844231071035, 0.8717680051749016, 0.865084012576853, 0.6973657956124407], 'avgF1': 0.7807252865248095, 'precision': [0.752, 0.7, 0.856, 0.808, 0.6987951807228916], 'avgPrecision': 0.7629590361445783, 'recall': [0.752, 0.7, 0.856, 0.808, 0.6987951807228916], 'avgRecall': 0.7629590361445783, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.664, 0.696, 0.836, 0.54, 0.6907630522088354], 'avgAccuracy': 0.6853526104417671, 'f1': [0.6723568334496085, 0.7124212523719164, 0.8612442871885596, 0.6199134199134199, 0.6926075159458129], 'avgF1': 0.7117086617738635, 'precision': [0.664, 0.696, 0.836, 0.54, 0.6907630522088354], 'avgPrecision': 0.6853526104417671, 'recall': [0.664, 0.696, 0.836, 0.54, 0.6907630522088354], 'avgRecall': 0.6853526104417671, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.656, 0.708, 0.844, 0.612, 0.6987951807228916], 'avgAccuracy': 0.7037590361445784, 'f1': [0.6610203927744278, 0.7196338108917473, 0.8618498318808755, 0.6861327472527473, 0.6995623519719906], 'avgF1': 0.7256398269543577, 'precision': [0.656, 0.708, 0.844, 0.612, 0.6987951807228916], 'avgPrecision': 0.7037590361445784, 'recall': [0.656, 0.708, 0.844, 0.612, 0.6987951807228916], 'avgRecall': 0.7037590361445784, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No, Year', 'accuracy': [0.808, 0.852, 0.928, 0.82, 0.8072289156626506], 'avgAccuracy': 0.8430457831325301, 'f1': [0.8091223188405796, 0.851848501046171, 0.9299194359576968, 0.8771341365461847, 0.8061349340012379], 'avgF1': 0.854831865278374, 'precision': [0.808, 0.852, 0.928, 0.82, 0.8072289156626506], 'avgPrecision': 0.8430457831325301, 'recall': [0.808, 0.852, 0.928, 0.82, 0.8072289156626506], 'avgRecall': 0.8430457831325301, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.835039  0.847844   0.835039  0.835039   
1  0.740546  0.758929   0.740546  0.740546   
2  0.693356  0.714041   0.693356  0.693356   
3  0.634059  0.604606   0.634059  0.634059   
4  0.713279  0.739526   0.713279  0.713279   
5  0.748546  0.765918   0.748546  0.748546   
6  0.685353  0.711709   0.685353  0.685353   
7  0.694962  0.718126   0.694962  0.694962   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.8, 0.848, 0.92, 0.808, 0.8072289156626506], 'avgAccuracy': 0.8366457831325301, 'f1': [0.80070144052166, 0.8510714886459209, 0.9218419753086421, 0.8729909909909911, 0.8071757751352362], 'avgF1': 0.85075633412049, 'precision': [0.8, 0.848, 0.92, 0.808, 0.8072289156626506], 'avgPrecision': 0.8366457831325301, 'recall': [0.8, 0.848, 0.92, 0.808, 0.8072289156626506], 'avgRecall': 0.8366457831325301, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.68, 0.796, 0.88, 0.78, 0.7108433734939759], 'avgAccuracy': 0.7693686746987952, 'f1': [0.686005575097896, 0.802802137049942, 0.8867666666666668, 0.8542647244028378, 0.7117175478526819], 'avgF1': 0.7883113302140049, 'precision': [0.68, 0.796, 0.88, 0.78, 0.7108433734939759], 'avgPrecision': 0.7693686746987952, 'recall': [0.68, 0.796, 0.88, 0.78, 0.7108433734939759], 'avgRecall': 0.7693686746987952, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.628, 0.696, 0.868, 0.568, 0.6827309236947792], 'avgAccuracy': 0.6885461847389558, 'f1': [0.6345123649576493, 0.7072758115057047, 0.8827566570545293, 0.6333938630790642, 0.6830701363663197], 'avgF1': 0.7082017665926534, 'precision': [0.628, 0.696, 0.868, 0.568, 0.6827309236947792], 'avgPrecision': 0.6885461847389558, 'recall': [0.628, 0.696, 0.868, 0.568, 0.6827309236947792], 'avgRecall': 0.6885461847389558, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgAccuracy': 0.634059437751004, 'f1': [0.5707754304175356, 0.6294854876782174, 0.874543583107165, 0.40804096073131607, 0.5401855099134929], 'avgF1': 0.6046061943695454, 'precision': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgPrecision': 0.634059437751004, 'recall': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgRecall': 0.634059437751004, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.732, 0.68, 0.856, 0.772, 0.6024096385542169], 'avgAccuracy': 0.7284819277108434, 'f1': [0.7390632788868725, 0.6951204458370609, 0.8657933260260626, 0.8514776632302405, 0.5975376841736061], 'avgF1': 0.7497984796307685, 'precision': [0.732, 0.68, 0.856, 0.772, 0.6024096385542169], 'avgPrecision': 0.7284819277108434, 'recall': [0.732, 0.68, 0.856, 0.772, 0.6024096385542169], 'avgRecall': 0.7284819277108434, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.764, 0.764, 0.812, 0.808, 0.7269076305220884], 'avgAccuracy': 0.7749815261044177, 'f1': [0.7674193595877806, 0.7721535145180023, 0.8307143557079862, 0.865084012576853, 0.7267242400706709], 'avgF1': 0.7924190964922586, 'precision': [0.764, 0.764, 0.812, 0.808, 0.7269076305220884], 'avgPrecision': 0.7749815261044177, 'recall': [0.764, 0.764, 0.812, 0.808, 0.7269076305220884], 'avgRecall': 0.7749815261044177, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.672, 0.7, 0.852, 0.512, 0.6666666666666666], 'avgAccuracy': 0.6805333333333333, 'f1': [0.6789272767171385, 0.7145027529453555, 0.8715000641083349, 0.5895916448052269, 0.6712397700509972], 'avgF1': 0.7051523017254105, 'precision': [0.672, 0.7, 0.852, 0.512, 0.6666666666666666], 'avgPrecision': 0.6805333333333333, 'recall': [0.672, 0.7, 0.852, 0.512, 0.6666666666666666], 'avgRecall': 0.6805333333333333, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.652, 0.708, 0.864, 0.584, 0.6867469879518072], 'avgAccuracy': 0.6989493975903615, 'f1': [0.6582158122065728, 0.7188953229626784, 0.8802854883777912, 0.6582358203614243, 0.6870039591614748], 'avgF1': 0.7205272806139883, 'precision': [0.652, 0.708, 0.864, 0.584, 0.6867469879518072], 'avgPrecision': 0.6989493975903615, 'recall': [0.652, 0.708, 0.864, 0.584, 0.6867469879518072], 'avgRecall': 0.6989493975903615, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles, Lab No', 'accuracy': [0.8, 0.848, 0.92, 0.808, 0.8072289156626506], 'avgAccuracy': 0.8366457831325301, 'f1': [0.80070144052166, 0.8510714886459209, 0.9218419753086421, 0.8729909909909911, 0.8071757751352362], 'avgF1': 0.85075633412049, 'precision': [0.8, 0.848, 0.92, 0.808, 0.8072289156626506], 'avgPrecision': 0.8366457831325301, 'recall': [0.8, 0.848, 0.92, 0.808, 0.8072289156626506], 'avgRecall': 0.8366457831325301, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.824627  0.839126   0.824627  0.824627   
1  0.753359  0.773109   0.753359  0.753359   
2  0.688546  0.708202   0.688546  0.688546   
3  0.634059  0.604606   0.634059  0.634059   
4  0.728482  0.749798   0.728482  0.728482   
5  0.741333  0.762462   0.741333  0.741333   
6  0.680533  0.705152   0.680533  0.680533   
7  0.697346  0.719548   0.697346  0.697346   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.792, 0.824, 0.884, 0.796, 0.7911646586345381], 'avgAccuracy': 0.8174329317269077, 'f1': [0.7949605594405594, 0.8263489198796828, 0.8899686383391229, 0.8587205387205387, 0.79171773801727], 'avgF1': 0.8323432788794347, 'precision': [0.792, 0.824, 0.884, 0.796, 0.7911646586345381], 'avgPrecision': 0.8174329317269077, 'recall': [0.792, 0.824, 0.884, 0.796, 0.7911646586345381], 'avgRecall': 0.8174329317269077, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.696, 0.748, 0.86, 0.776, 0.7269076305220884], 'avgAccuracy': 0.7613815261044177, 'f1': [0.7031617286358225, 0.7563196578028969, 0.8771813051146384, 0.8455571250519319, 0.7284514979772486], 'avgF1': 0.7821342629165077, 'precision': [0.696, 0.748, 0.86, 0.776, 0.7269076305220884], 'avgPrecision': 0.7613815261044177, 'recall': [0.696, 0.748, 0.86, 0.776, 0.7269076305220884], 'avgRecall': 0.7613815261044177, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.628, 0.712, 0.864, 0.564, 0.6546184738955824], 'avgAccuracy': 0.6845236947791165, 'f1': [0.6353872127872128, 0.7200649313473609, 0.8794031538527503, 0.6211590458968398, 0.6605680976765315], 'avgF1': 0.703316488312139, 'precision': [0.628, 0.712, 0.864, 0.564, 0.6546184738955824], 'avgPrecision': 0.6845236947791165, 'recall': [0.628, 0.712, 0.864, 0.564, 0.6546184738955824], 'avgRecall': 0.6845236947791165, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgAccuracy': 0.634059437751004, 'f1': [0.5707754304175356, 0.6294854876782174, 0.874543583107165, 0.40804096073131607, 0.5401855099134929], 'avgF1': 0.6046061943695454, 'precision': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgPrecision': 0.634059437751004, 'recall': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgRecall': 0.634059437751004, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.78, 0.764, 0.856, 0.644, 0.6385542168674698], 'avgAccuracy': 0.7365108433734939, 'f1': [0.7804266666666666, 0.7752622307383256, 0.8746518542133269, 0.735195867806372, 0.636527555211246], 'avgF1': 0.7604128349271875, 'precision': [0.78, 0.764, 0.856, 0.644, 0.6385542168674698], 'avgPrecision': 0.7365108433734939, 'recall': [0.78, 0.764, 0.856, 0.644, 0.6385542168674698], 'avgRecall': 0.7365108433734939, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.76, 0.788, 0.82, 0.744, 0.7309236947791165], 'avgAccuracy': 0.7685847389558232, 'f1': [0.7668811322776841, 0.7941536482618903, 0.8376915981293339, 0.8142614770459081, 0.7310036254257585], 'avgF1': 0.7887982962281149, 'precision': [0.76, 0.788, 0.82, 0.744, 0.7309236947791165], 'avgPrecision': 0.7685847389558232, 'recall': [0.76, 0.788, 0.82, 0.744, 0.7309236947791165], 'avgRecall': 0.7685847389558232, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.672, 0.708, 0.852, 0.54, 0.6666666666666666], 'avgAccuracy': 0.6877333333333333, 'f1': [0.6794618253837268, 0.7226079999999999, 0.8715000641083349, 0.6176410256410256, 0.6701579496986841], 'avgF1': 0.7122737729663543, 'precision': [0.672, 0.708, 0.852, 0.54, 0.6666666666666666], 'avgPrecision': 0.6877333333333333, 'recall': [0.672, 0.708, 0.852, 0.54, 0.6666666666666666], 'avgRecall': 0.6877333333333333, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.656, 0.72, 0.872, 0.58, 0.7670682730923695], 'avgAccuracy': 0.7190136546184739, 'f1': [0.6618865430214558, 0.7316342053318208, 0.8880875114571823, 0.640099242845532, 0.7666650256782749], 'avgF1': 0.7376745056668531, 'precision': [0.656, 0.72, 0.872, 0.58, 0.7670682730923695], 'avgPrecision': 0.7190136546184739, 'recall': [0.656, 0.72, 0.872, 0.58, 0.7670682730923695], 'avgRecall': 0.7190136546184739, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age, Crypt profiles', 'accuracy': [0.792, 0.824, 0.884, 0.796, 0.7911646586345381], 'avgAccuracy': 0.8174329317269077, 'f1': [0.7949605594405594, 0.8263489198796828, 0.8899686383391229, 0.8587205387205387, 0.79171773801727], 'avgF1': 0.8323432788794347, 'precision': [0.792, 0.824, 0.884, 0.796, 0.7911646586345381], 'avgPrecision': 0.8174329317269077, 'recall': [0.792, 0.824, 0.884, 0.796, 0.7911646586345381], 'avgRecall': 0.8174329317269077, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.815039  0.830581   0.815039  0.815039   
1  0.752578  0.774817   0.752578  0.752578   
2  0.684524  0.703316   0.684524  0.684524   
3  0.634059  0.604606   0.634059  0.634059   
4  0.724431  0.740461   0.724431  0.724431   
5  0.748559  0.765927   0.748559  0.748559   
6  0.687733  0.712274   0.687733  0.687733   
7  0.717407  0.737534   0.717407  0.717407   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.776, 0.828, 0.888, 0.796, 0.7951807228915663], 'avgAccuracy': 0.8166361445783132, 'f1': [0.7799815388004626, 0.830299790396708, 0.894576374230913, 0.8600628956503982, 0.7953062999960308], 'avgF1': 0.8320453798149025, 'precision': [0.776, 0.828, 0.888, 0.796, 0.7951807228915663], 'avgPrecision': 0.8166361445783132, 'recall': [0.776, 0.828, 0.888, 0.796, 0.7951807228915663], 'avgRecall': 0.8166361445783132, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.692, 0.752, 0.86, 0.776, 0.7269076305220884], 'avgAccuracy': 0.7613815261044177, 'f1': [0.6992379000632511, 0.7599011904761905, 0.8771813051146384, 0.8455571250519319, 0.7284514979772486], 'avgF1': 0.7820658037366521, 'precision': [0.692, 0.752, 0.86, 0.776, 0.7269076305220884], 'avgPrecision': 0.7613815261044177, 'recall': [0.692, 0.752, 0.86, 0.776, 0.7269076305220884], 'avgRecall': 0.7613815261044177, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.628, 0.716, 0.864, 0.568, 0.6546184738955824], 'avgAccuracy': 0.6861236947791165, 'f1': [0.6356269439252479, 0.7244374009829885, 0.8794031538527503, 0.6253061224489797, 0.6605680976765315], 'avgF1': 0.7050683437772995, 'precision': [0.628, 0.716, 0.864, 0.568, 0.6546184738955824], 'avgPrecision': 0.6861236947791165, 'recall': [0.628, 0.716, 0.864, 0.568, 0.6546184738955824], 'avgRecall': 0.6861236947791165, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.608, 0.672, 0.888, 0.428, 0.570281124497992], 'avgAccuracy': 0.6332562248995984, 'f1': [0.5707754304175356, 0.6294854876782174, 0.874543583107165, 0.40804096073131607, 0.532866198780429], 'avgF1': 0.6031423321429326, 'precision': [0.608, 0.672, 0.888, 0.428, 0.570281124497992], 'avgPrecision': 0.6332562248995984, 'recall': [0.608, 0.672, 0.888, 0.428, 0.570281124497992], 'avgRecall': 0.6332562248995984, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.772, 0.764, 0.856, 0.644, 0.6385542168674698], 'avgAccuracy': 0.734910843373494, 'f1': [0.7721947826086957, 0.7752622307383256, 0.8746518542133269, 0.735195867806372, 0.636527555211246], 'avgF1': 0.7587664581155933, 'precision': [0.772, 0.764, 0.856, 0.644, 0.6385542168674698], 'avgPrecision': 0.734910843373494, 'recall': [0.772, 0.764, 0.856, 0.644, 0.6385542168674698], 'avgRecall': 0.734910843373494, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.728, 0.748, 0.84, 0.82, 0.7068273092369478], 'avgAccuracy': 0.7685654618473895, 'f1': [0.7308405852909475, 0.7535628356646519, 0.8557235485641119, 0.8752727272727272, 0.7068548392647183], 'avgF1': 0.7844509072114314, 'precision': [0.728, 0.748, 0.84, 0.82, 0.7068273092369478], 'avgPrecision': 0.7685654618473895, 'recall': [0.728, 0.748, 0.84, 0.82, 0.7068273092369478], 'avgRecall': 0.7685654618473895, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.672, 0.708, 0.852, 0.532, 0.6626506024096386], 'avgAccuracy': 0.6853301204819278, 'f1': [0.6792153443766347, 0.7226079999999999, 0.8715000641083349, 0.6075790875790876, 0.6660327536160839], 'avgF1': 0.7093870499360282, 'precision': [0.672, 0.708, 0.852, 0.532, 0.6626506024096386], 'avgPrecision': 0.6853301204819278, 'recall': [0.672, 0.708, 0.852, 0.532, 0.6626506024096386], 'avgRecall': 0.6853301204819278, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.644, 0.724, 0.868, 0.572, 0.7590361445783133], 'avgAccuracy': 0.7134072289156627, 'f1': [0.6501753343115089, 0.7364096918417384, 0.8848278612591473, 0.635043340357585, 0.7589686477000439], 'avgF1': 0.7330849750940047, 'precision': [0.644, 0.724, 0.868, 0.572, 0.7590361445783133], 'avgPrecision': 0.7134072289156627, 'recall': [0.644, 0.724, 0.868, 0.572, 0.7590361445783133], 'avgRecall': 0.7134072289156627, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes, Age', 'accuracy': [0.776, 0.828, 0.888, 0.796, 0.7951807228915663], 'avgAccuracy': 0.8166361445783132, 'f1': [0.7799815388004626, 0.830299790396708, 0.894576374230913, 0.8600628956503982, 0.7953062999960308], 'avgF1': 0.8320453798149025, 'precision': [0.776, 0.828, 0.888, 0.796, 0.7951807228915663], 'avgPrecision': 0.8166361445783132, 'recall': [0.776, 0.828, 0.888, 0.796, 0.7951807228915663], 'avgRecall': 0.8166361445783132, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.814233  0.829368   0.814233  0.814233   
1  0.754185  0.776145   0.754185  0.754185   
2  0.686124  0.705068   0.686124  0.686124   
3  0.633256  0.603142   0.633256  0.633256   
4  0.716431  0.733125   0.716431  0.716431   
5  0.753385  0.774320   0.753385  0.753385   
6  0.685330  0.709387   0.685330  0.685330   
7  0.690124  0.710263   0.690124  0.690124   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.768, 0.844, 0.924, 0.776, 0.7751004016064257], 'avgAccuracy': 0.8174200803212851, 'f1': [0.7710381914595954, 0.8467171433294759, 0.9269969418960246, 0.845307829255165, 0.7757811496769269], 'avgF1': 0.8331682511234375, 'precision': [0.768, 0.844, 0.924, 0.776, 0.7751004016064257], 'avgPrecision': 0.8174200803212851, 'recall': [0.768, 0.844, 0.924, 0.776, 0.7751004016064257], 'avgRecall': 0.8174200803212851, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.716, 0.78, 0.9, 0.756, 0.7269076305220884], 'avgAccuracy': 0.7757815261044176, 'f1': [0.721270182334316, 0.7884234595234174, 0.907281649436546, 0.8322401183682097, 0.7275785619421031], 'avgF1': 0.7953587943209184, 'precision': [0.716, 0.78, 0.9, 0.756, 0.7269076305220884], 'avgPrecision': 0.7757815261044176, 'recall': [0.716, 0.78, 0.9, 0.756, 0.7269076305220884], 'avgRecall': 0.7757815261044176, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 15, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.648, 0.716, 0.86, 0.572, 0.6465863453815262], 'avgAccuracy': 0.6885172690763052, 'f1': [0.6554516129032258, 0.7263299958120096, 0.8762467524878655, 0.6310204081632653, 0.6510610286524516], 'avgF1': 0.7080219596037636, 'precision': [0.648, 0.716, 0.86, 0.572, 0.6465863453815262], 'avgPrecision': 0.6885172690763052, 'recall': [0.648, 0.716, 0.86, 0.572, 0.6465863453815262], 'avgRecall': 0.6885172690763052, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgAccuracy': 0.634059437751004, 'f1': [0.5707754304175356, 0.6294854876782174, 0.874543583107165, 0.40804096073131607, 0.5401855099134929], 'avgF1': 0.6046061943695454, 'precision': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgPrecision': 0.634059437751004, 'recall': [0.608, 0.672, 0.888, 0.428, 0.5742971887550201], 'avgRecall': 0.634059437751004, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.704, 0.768, 0.896, 0.648, 0.5180722891566265], 'avgAccuracy': 0.7068144578313253, 'f1': [0.7135004784688995, 0.7794531480889282, 0.9025499385196132, 0.7438452661219592, 0.45668759284929633], 'avgF1': 0.7192072848097393, 'precision': [0.704, 0.768, 0.896, 0.648, 0.5180722891566265], 'avgPrecision': 0.7068144578313253, 'recall': [0.704, 0.768, 0.896, 0.648, 0.5180722891566265], 'avgRecall': 0.7068144578313253, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.72, 0.836, 0.888, 0.776, 0.7228915662650602], 'avgAccuracy': 0.7885783132530121, 'f1': [0.7236913898520245, 0.8418518258215861, 0.8939177439010371, 0.845307829255165, 0.7233361737406712], 'avgF1': 0.8056209925140968, 'precision': [0.72, 0.836, 0.888, 0.776, 0.7228915662650602], 'avgPrecision': 0.7885783132530121, 'recall': [0.72, 0.836, 0.888, 0.776, 0.7228915662650602], 'avgRecall': 0.7885783132530121, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.664, 0.708, 0.852, 0.464, 0.6626506024096386], 'avgAccuracy': 0.6701301204819277, 'f1': [0.6718798222429734, 0.7226079999999999, 0.8715000641083349, 0.532545073375262, 0.6660327536160839], 'avgF1': 0.6929131426685309, 'precision': [0.664, 0.708, 0.852, 0.464, 0.6626506024096386], 'avgPrecision': 0.6701301204819277, 'recall': [0.664, 0.708, 0.852, 0.464, 0.6626506024096386], 'avgRecall': 0.6701301204819277, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.648, 0.72, 0.86, 0.576, 0.6586345381526104], 'avgAccuracy': 0.692526907630522, 'f1': [0.6547755102040816, 0.7329482224569688, 0.8785069032400624, 0.641904761904762, 0.6621327328068238], 'avgF1': 0.7140536261225398, 'precision': [0.648, 0.72, 0.86, 0.576, 0.6586345381526104], 'avgPrecision': 0.692526907630522, 'recall': [0.648, 0.72, 0.86, 0.576, 0.6586345381526104], 'avgRecall': 0.692526907630522, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?, Intraepithelial lymphocytes', 'accuracy': [0.768, 0.844, 0.924, 0.776, 0.7751004016064257], 'avgAccuracy': 0.8174200803212851, 'f1': [0.7710381914595954, 0.8467171433294759, 0.9269969418960246, 0.845307829255165, 0.7757811496769269], 'avgF1': 0.8331682511234375, 'precision': [0.768, 0.844, 0.924, 0.776, 0.7751004016064257], 'avgPrecision': 0.8174200803212851, 'recall': [0.768, 0.844, 0.924, 0.776, 0.7751004016064257], 'avgRecall': 0.8174200803212851, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.812614  0.828674   0.812614  0.812614   
1  0.766985  0.787561   0.766985  0.766985   
2  0.688517  0.708022   0.688517  0.688517   
3  0.634059  0.604606   0.634059  0.634059   
4  0.698786  0.704359   0.698786  0.698786   
5  0.774982  0.793803   0.774982  0.774982   
6  0.670130  0.692913   0.670130  0.670130   
7  0.692527  0.714054   0.692527  0.692527   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.756, 0.848, 0.924, 0.784, 0.7670682730923695], 'avgAccuracy': 0.8158136546184739, 'f1': [0.7585329884475288, 0.8503062200956938, 0.9269969418960246, 0.8507948517619968, 0.7679919332822694], 'avgF1': 0.8309245870967027, 'precision': [0.756, 0.848, 0.924, 0.784, 0.7670682730923695], 'avgPrecision': 0.8158136546184739, 'recall': [0.756, 0.848, 0.924, 0.784, 0.7670682730923695], 'avgRecall': 0.8158136546184739, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.728, 0.792, 0.896, 0.752, 0.714859437751004], 'avgAccuracy': 0.7765718875502008, 'f1': [0.7326868326028833, 0.7993282856278977, 0.9040552752293578, 0.8301371610845295, 0.7158157757140254], 'avgF1': 0.7964046660517388, 'precision': [0.728, 0.792, 0.896, 0.752, 0.714859437751004], 'avgPrecision': 0.7765718875502008, 'recall': [0.728, 0.792, 0.896, 0.752, 0.714859437751004], 'avgRecall': 0.7765718875502008, 'params': [{'algorithm': 'kd_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.648, 0.716, 0.86, 0.564, 0.6465863453815262], 'avgAccuracy': 0.6869172690763052, 'f1': [0.6554516129032258, 0.7263299958120096, 0.8762467524878655, 0.6240121686223381, 0.6510610286524516], 'avgF1': 0.7066203116955782, 'precision': [0.648, 0.716, 0.86, 0.564, 0.6465863453815262], 'avgPrecision': 0.6869172690763052, 'recall': [0.648, 0.716, 0.86, 0.564, 0.6465863453815262], 'avgRecall': 0.6869172690763052, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.62, 0.688, 0.884, 0.428, 0.5742971887550201], 'avgAccuracy': 0.638859437751004, 'f1': [0.581380795874828, 0.643705731394354, 0.8702176302289862, 0.40804096073131607, 0.5401251666311907], 'avgF1': 0.6086940569721351, 'precision': [0.62, 0.688, 0.884, 0.428, 0.5742971887550201], 'avgPrecision': 0.638859437751004, 'recall': [0.62, 0.688, 0.884, 0.428, 0.5742971887550201], 'avgRecall': 0.638859437751004, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.66, 0.796, 0.892, 0.636, 0.5461847389558233], 'avgAccuracy': 0.7060369477911647, 'f1': [0.6688446527620913, 0.8032027143040741, 0.9002468756319515, 0.734302439938182, 0.5279736630174927], 'avgF1': 0.7269140691307583, 'precision': [0.66, 0.796, 0.892, 0.636, 0.5461847389558233], 'avgPrecision': 0.7060369477911647, 'recall': [0.66, 0.796, 0.892, 0.636, 0.5461847389558233], 'avgRecall': 0.7060369477911647, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.752, 0.792, 0.908, 0.78, 0.7068273092369478], 'avgAccuracy': 0.7877654618473896, 'f1': [0.7553572336338111, 0.7981570091112958, 0.9126948628607164, 0.8480607360950652, 0.7073195707415435], 'avgF1': 0.8043178824884865, 'precision': [0.752, 0.792, 0.908, 0.78, 0.7068273092369478], 'avgPrecision': 0.7877654618473896, 'recall': [0.752, 0.792, 0.908, 0.78, 0.7068273092369478], 'avgRecall': 0.7877654618473896, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.668, 0.708, 0.852, 0.452, 0.6626506024096386], 'avgAccuracy': 0.6685301204819277, 'f1': [0.6760727778392696, 0.7226079999999999, 0.8715000641083349, 0.5168910154173311, 0.6660327536160839], 'avgF1': 0.6906209221962039, 'precision': [0.668, 0.708, 0.852, 0.452, 0.6626506024096386], 'avgPrecision': 0.6685301204819277, 'recall': [0.668, 0.708, 0.852, 0.452, 0.6626506024096386], 'avgRecall': 0.6685301204819277, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.648, 0.728, 0.856, 0.576, 0.6626506024096386], 'avgAccuracy': 0.6941301204819277, 'f1': [0.6550349886975367, 0.7349578957049573, 0.8750948769992694, 0.641904761904762, 0.6661219207106958], 'avgF1': 0.7146228888034443, 'precision': [0.648, 0.728, 0.856, 0.576, 0.6626506024096386], 'avgPrecision': 0.6941301204819277, 'recall': [0.648, 0.728, 0.856, 0.576, 0.6626506024096386], 'avgRecall': 0.6941301204819277, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?, Mild & superficial increase in lamina propria cellularity?', 'accuracy': [0.756, 0.848, 0.924, 0.784, 0.7670682730923695], 'avgAccuracy': 0.8158136546184739, 'f1': [0.7585329884475288, 0.8503062200956938, 0.9269969418960246, 0.8507948517619968, 0.7679919332822694], 'avgF1': 0.8309245870967027, 'precision': [0.756, 0.848, 0.924, 0.784, 0.7670682730923695], 'avgPrecision': 0.8158136546184739, 'recall': [0.756, 0.848, 0.924, 0.784, 0.7670682730923695], 'avgRecall': 0.8158136546184739, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.810204  0.825954   0.810204  0.810204   
1  0.766978  0.787650   0.766978  0.766978   
2  0.686917  0.706620   0.686917  0.686917   
3  0.638859  0.608694   0.638859  0.638859   
4  0.701986  0.707717   0.701986  0.701986   
5  0.746059  0.761040   0.746059  0.746059   
6  0.668530  0.690621   0.668530  0.668530   
7  0.691727  0.712105   0.691727  0.691727   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.768, 0.844, 0.924, 0.772, 0.7670682730923695], 'avgAccuracy': 0.8150136546184739, 'f1': [0.7704476266785957, 0.846003005510102, 0.9269969418960246, 0.8425359368508517, 0.7679919332822694], 'avgF1': 0.8307950888435687, 'precision': [0.768, 0.844, 0.924, 0.772, 0.7670682730923695], 'avgPrecision': 0.8150136546184739, 'recall': [0.768, 0.844, 0.924, 0.772, 0.7670682730923695], 'avgRecall': 0.8150136546184739, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.724, 0.78, 0.896, 0.752, 0.7269076305220884], 'avgAccuracy': 0.7757815261044176, 'f1': [0.7288879120879121, 0.7893167172784931, 0.9040552752293578, 0.8301371610845295, 0.7279149078730333], 'avgF1': 0.7960623947106652, 'precision': [0.724, 0.78, 0.896, 0.752, 0.7269076305220884], 'avgPrecision': 0.7757815261044176, 'recall': [0.724, 0.78, 0.896, 0.752, 0.7269076305220884], 'avgRecall': 0.7757815261044176, 'params': [{'algorithm': 'kd_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 13, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.648, 0.716, 0.86, 0.572, 0.6506024096385542], 'avgAccuracy': 0.6893204819277108, 'f1': [0.6554516129032258, 0.7272613636363636, 0.8762467524878655, 0.6322930402930402, 0.6550214204824703], 'avgF1': 0.7092548379605931, 'precision': [0.648, 0.716, 0.86, 0.572, 0.6506024096385542], 'avgPrecision': 0.6893204819277108, 'recall': [0.648, 0.716, 0.86, 0.572, 0.6506024096385542], 'avgRecall': 0.6893204819277108, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.616, 0.704, 0.888, 0.404, 0.5662650602409639], 'avgAccuracy': 0.6356530120481928, 'f1': [0.5735620650617654, 0.6564750813145214, 0.8722063492063492, 0.37117381745654, 0.5211117031664781], 'avgF1': 0.5989058032411309, 'precision': [0.616, 0.704, 0.888, 0.404, 0.5662650602409639], 'avgPrecision': 0.6356530120481928, 'recall': [0.616, 0.704, 0.888, 0.404, 0.5662650602409639], 'avgRecall': 0.6356530120481928, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.66, 0.796, 0.892, 0.636, 0.5461847389558233], 'avgAccuracy': 0.7060369477911647, 'f1': [0.6688446527620913, 0.8032027143040741, 0.9002468756319515, 0.734302439938182, 0.5279736630174927], 'avgF1': 0.7269140691307583, 'precision': [0.66, 0.796, 0.892, 0.636, 0.5461847389558233], 'avgPrecision': 0.7060369477911647, 'recall': [0.66, 0.796, 0.892, 0.636, 0.5461847389558233], 'avgRecall': 0.7060369477911647, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.716, 0.772, 0.916, 0.808, 0.714859437751004], 'avgAccuracy': 0.7853718875502008, 'f1': [0.7205241708528768, 0.7783140976402435, 0.919509762408845, 0.8657239057239058, 0.7155780016031595], 'avgF1': 0.7999299876458061, 'precision': [0.716, 0.772, 0.916, 0.808, 0.714859437751004], 'avgPrecision': 0.7853718875502008, 'recall': [0.716, 0.772, 0.916, 0.808, 0.714859437751004], 'avgRecall': 0.7853718875502008, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.668, 0.708, 0.852, 0.48, 0.6666666666666666], 'avgAccuracy': 0.6749333333333333, 'f1': [0.6760727778392696, 0.7226079999999999, 0.8715000641083349, 0.5527407407407406, 0.6703791097289926], 'avgF1': 0.6986601384834675, 'precision': [0.668, 0.708, 0.852, 0.48, 0.6666666666666666], 'avgPrecision': 0.6749333333333333, 'recall': [0.668, 0.708, 0.852, 0.48, 0.6666666666666666], 'avgRecall': 0.6749333333333333, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.68, 0.724, 0.852, 0.584, 0.7269076305220884], 'avgAccuracy': 0.7133815261044176, 'f1': [0.6862668400052483, 0.7378672476191743, 0.8716472151200682, 0.6483540802213001, 0.7275471115230151], 'avgF1': 0.7343364988977612, 'precision': [0.68, 0.724, 0.852, 0.584, 0.7269076305220884], 'avgPrecision': 0.7133815261044176, 'recall': [0.68, 0.724, 0.852, 0.584, 0.7269076305220884], 'avgRecall': 0.7133815261044176, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation, Increased lymphoid aggregates in lamina propria?', 'accuracy': [0.768, 0.844, 0.924, 0.772, 0.7670682730923695], 'avgAccuracy': 0.8150136546184739, 'f1': [0.7704476266785957, 0.846003005510102, 0.9269969418960246, 0.8425359368508517, 0.7679919332822694], 'avgF1': 0.8307950888435687, 'precision': [0.768, 0.844, 0.924, 0.772, 0.7670682730923695], 'avgPrecision': 0.8150136546184739, 'recall': [0.768, 0.844, 0.924, 0.772, 0.7670682730923695], 'avgRecall': 0.8150136546184739, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.811814  0.827935   0.811814  0.811814   
1  0.766178  0.786985   0.766178  0.766178   
2  0.689320  0.709255   0.689320  0.689320   
3  0.635653  0.598906   0.635653  0.635653   
4  0.673986  0.681078   0.673986  0.673986   
5  0.768540  0.784364   0.768540  0.768540   
6  0.674933  0.698660   0.674933  0.674933   
7  0.692520  0.713196   0.692520  0.692520   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.76, 0.852, 0.924, 0.776, 0.7710843373493976], 'avgAccuracy': 0.8166168674698795, 'f1': [0.762290234628267, 0.8545801289469334, 0.9269969418960246, 0.845307829255165, 0.7720307310067162], 'avgF1': 0.8322411731466212, 'precision': [0.76, 0.852, 0.924, 0.776, 0.7710843373493976], 'avgPrecision': 0.8166168674698795, 'recall': [0.76, 0.852, 0.924, 0.776, 0.7710843373493976], 'avgRecall': 0.8166168674698795, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.732, 0.792, 0.896, 0.756, 0.7188755020080321], 'avgAccuracy': 0.7789751004016064, 'f1': [0.736479876092488, 0.7985600615029791, 0.9040552752293578, 0.8330069930069929, 0.7196278838921152], 'avgF1': 0.7983460179447865, 'precision': [0.732, 0.792, 0.896, 0.756, 0.7188755020080321], 'avgPrecision': 0.7789751004016064, 'recall': [0.732, 0.792, 0.896, 0.756, 0.7188755020080321], 'avgRecall': 0.7789751004016064, 'params': [{'algorithm': 'kd_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.648, 0.72, 0.86, 0.572, 0.6506024096385542], 'avgAccuracy': 0.6901204819277108, 'f1': [0.6554516129032258, 0.7308113236128395, 0.8762467524878655, 0.6322930402930402, 0.6550214204824703], 'avgF1': 0.7099648299558883, 'precision': [0.648, 0.72, 0.86, 0.572, 0.6506024096385542], 'avgPrecision': 0.6901204819277108, 'recall': [0.648, 0.72, 0.86, 0.572, 0.6506024096385542], 'avgRecall': 0.6901204819277108, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.616, 0.7, 0.884, 0.404, 0.5662650602409639], 'avgAccuracy': 0.6340530120481928, 'f1': [0.5735620650617654, 0.6531349914158526, 0.8679138562966777, 0.37117381745654, 0.5211117031664781], 'avgF1': 0.5973792866794628, 'precision': [0.616, 0.7, 0.884, 0.404, 0.5662650602409639], 'avgPrecision': 0.6340530120481928, 'recall': [0.616, 0.7, 0.884, 0.404, 0.5662650602409639], 'avgRecall': 0.6340530120481928, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.7, 0.704, 0.888, 0.744, 0.5502008032128514], 'avgAccuracy': 0.7172401606425702, 'f1': [0.7078155573376103, 0.7212597266424712, 0.8968246927479125, 0.8222207061231452, 0.5180001150463434], 'avgF1': 0.7332241595794965, 'precision': [0.7, 0.704, 0.888, 0.744, 0.5502008032128514], 'avgPrecision': 0.7172401606425702, 'recall': [0.7, 0.704, 0.888, 0.744, 0.5502008032128514], 'avgRecall': 0.7172401606425702, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.696, 0.836, 0.896, 0.792, 0.7188755020080321], 'avgAccuracy': 0.7877751004016064, 'f1': [0.7017341845919591, 0.8411793035702716, 0.9047409472996044, 0.8548417508417508, 0.7191744321944609], 'avgF1': 0.8043341236996093, 'precision': [0.696, 0.836, 0.896, 0.792, 0.7188755020080321], 'avgPrecision': 0.7877751004016064, 'recall': [0.696, 0.836, 0.896, 0.792, 0.7188755020080321], 'avgRecall': 0.7877751004016064, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.668, 0.716, 0.852, 0.452, 0.6666666666666666], 'avgAccuracy': 0.6709333333333334, 'f1': [0.6758187399996993, 0.7306739065930805, 0.8715000641083349, 0.5168910154173311, 0.6703791097289926], 'avgF1': 0.6930525671694877, 'precision': [0.668, 0.716, 0.852, 0.452, 0.6666666666666666], 'avgPrecision': 0.6709333333333334, 'recall': [0.668, 0.716, 0.852, 0.452, 0.6666666666666666], 'avgRecall': 0.6709333333333334, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.644, 0.724, 0.852, 0.58, 0.7469879518072289], 'avgAccuracy': 0.7093975903614458, 'f1': [0.6511227293379043, 0.7332682711104634, 0.8716472151200682, 0.6456596858638745, 0.7477330028239607], 'avgF1': 0.7298861808512542, 'precision': [0.644, 0.724, 0.852, 0.58, 0.7469879518072289], 'avgPrecision': 0.7093975903614458, 'recall': [0.644, 0.724, 0.852, 0.58, 0.7469879518072289], 'avgRecall': 0.7093975903614458, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex, Method of confirmation', 'accuracy': [0.76, 0.852, 0.924, 0.776, 0.7710843373493976], 'avgAccuracy': 0.8166168674698795, 'f1': [0.762290234628267, 0.8545801289469334, 0.9269969418960246, 0.845307829255165, 0.7720307310067162], 'avgF1': 0.8322411731466212, 'precision': [0.76, 0.852, 0.924, 0.776, 0.7710843373493976], 'avgPrecision': 0.8166168674698795, 'recall': [0.76, 0.852, 0.924, 0.776, 0.7710843373493976], 'avgRecall': 0.8166168674698795, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.809410  0.825589   0.809410  0.809410   
1  0.768582  0.789086   0.768582  0.768582   
2  0.690120  0.709965   0.690120  0.690120   
3  0.634053  0.597379   0.634053  0.634053   
4  0.700398  0.713536   0.700398  0.700398   
5  0.778149  0.794026   0.778149  0.778149   
6  0.670933  0.693053   0.670933  0.670933   
7  0.690927  0.711864   0.690927  0.690927   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.744, 0.848, 0.924, 0.768, 0.7630522088353414], 'avgAccuracy': 0.8094104417670683, 'f1': [0.7463717971229823, 0.8503855874500309, 0.9269969418960246, 0.8363025210084033, 0.7641752014959488], 'avgF1': 0.8248464097946779, 'precision': [0.744, 0.848, 0.924, 0.768, 0.7630522088353414], 'avgPrecision': 0.8094104417670683, 'recall': [0.744, 0.848, 0.924, 0.768, 0.7630522088353414], 'avgRecall': 0.8094104417670683, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.716, 0.788, 0.908, 0.744, 0.7188755020080321], 'avgAccuracy': 0.7749751004016064, 'f1': [0.7212391911005773, 0.7948800000000001, 0.9135055481104262, 0.8200202634245187, 0.7199058034136736], 'avgF1': 0.7939101612098391, 'precision': [0.716, 0.788, 0.908, 0.744, 0.7188755020080321], 'avgPrecision': 0.7749751004016064, 'recall': [0.716, 0.788, 0.908, 0.744, 0.7188755020080321], 'avgRecall': 0.7749751004016064, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 15, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.648, 0.728, 0.86, 0.572, 0.6506024096385542], 'avgAccuracy': 0.6917204819277109, 'f1': [0.6554516129032258, 0.7379098681129588, 0.8776868010258023, 0.6281587834250154, 0.6550214204824703], 'avgF1': 0.7108456971898945, 'precision': [0.648, 0.728, 0.86, 0.572, 0.6506024096385542], 'avgPrecision': 0.6917204819277109, 'recall': [0.648, 0.728, 0.86, 0.572, 0.6506024096385542], 'avgRecall': 0.6917204819277109, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.616, 0.704, 0.884, 0.404, 0.5622489959839357], 'avgAccuracy': 0.6340497991967872, 'f1': [0.5735620650617654, 0.6566827155717494, 0.8679138562966777, 0.37117381745654, 0.518094152995445], 'avgF1': 0.5974853214764355, 'precision': [0.616, 0.704, 0.884, 0.404, 0.5622489959839357], 'avgPrecision': 0.6340497991967872, 'recall': [0.616, 0.704, 0.884, 0.404, 0.5622489959839357], 'avgRecall': 0.6340497991967872, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.692, 0.808, 0.884, 0.692, 0.5542168674698795], 'avgAccuracy': 0.7260433734939759, 'f1': [0.7009594377510041, 0.814857962697274, 0.8951970210072948, 0.7735882352941178, 0.5269321289585843], 'avgF1': 0.742306957141655, 'precision': [0.692, 0.808, 0.884, 0.692, 0.5542168674698795], 'avgPrecision': 0.7260433734939759, 'recall': [0.692, 0.808, 0.884, 0.692, 0.5542168674698795], 'avgRecall': 0.7260433734939759, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.784, 0.812, 0.88, 0.752, 0.6867469879518072], 'avgAccuracy': 0.7829493975903614, 'f1': [0.7882946941579232, 0.8163384364571654, 0.8898300213390236, 0.8213663259817106, 0.6857127184371647], 'avgF1': 0.8003084392745975, 'precision': [0.784, 0.812, 0.88, 0.752, 0.6867469879518072], 'avgPrecision': 0.7829493975903614, 'recall': [0.784, 0.812, 0.88, 0.752, 0.6867469879518072], 'avgRecall': 0.7829493975903614, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.668, 0.712, 0.856, 0.42, 0.6666666666666666], 'avgAccuracy': 0.6645333333333333, 'f1': [0.6758187399996993, 0.7256579009713336, 0.8740694671919496, 0.4743322493676867, 0.6703791097289926], 'avgF1': 0.6840514934519324, 'precision': [0.668, 0.712, 0.856, 0.42, 0.6666666666666666], 'avgPrecision': 0.6645333333333333, 'recall': [0.668, 0.712, 0.856, 0.42, 0.6666666666666666], 'avgRecall': 0.6645333333333333, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.676, 0.724, 0.856, 0.584, 0.6626506024096386], 'avgAccuracy': 0.7005301204819278, 'f1': [0.6815562608512024, 0.733585989767808, 0.8742227642276423, 0.6499978340914015, 0.6661219207106958], 'avgF1': 0.72109695392975, 'precision': [0.676, 0.724, 0.856, 0.584, 0.6626506024096386], 'avgPrecision': 0.7005301204819278, 'recall': [0.676, 0.724, 0.856, 0.584, 0.6626506024096386], 'avgRecall': 0.7005301204819278, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells, Sex', 'accuracy': [0.744, 0.848, 0.924, 0.768, 0.7630522088353414], 'avgAccuracy': 0.8094104417670683, 'f1': [0.7463717971229823, 0.8503855874500309, 0.9269969418960246, 0.8363025210084033, 0.7641752014959488], 'avgF1': 0.8248464097946779, 'precision': [0.744, 0.848, 0.924, 0.768, 0.7630522088353414], 'avgPrecision': 0.8094104417670683, 'recall': [0.744, 0.848, 0.924, 0.768, 0.7630522088353414], 'avgRecall': 0.8094104417670683, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.807007  0.822503   0.807007  0.807007   
1  0.766178  0.786435   0.766178  0.766178   
2  0.691720  0.710846   0.691720  0.691720   
3  0.634050  0.597485   0.634050  0.634050   
4  0.692386  0.702934   0.692386  0.692386   
5  0.752565  0.773807   0.752565  0.752565   
6  0.664533  0.684051   0.664533  0.664533   
7  0.691724  0.711607   0.691724  0.691724   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.76, 0.828, 0.924, 0.744, 0.7429718875502008], 'avgAccuracy': 0.7997943775100401, 'f1': [0.7629060838668136, 0.8297918332727493, 0.9270842081345148, 0.8124973292793163, 0.7440280182396863], 'avgF1': 0.8152614945586161, 'precision': [0.76, 0.828, 0.924, 0.744, 0.7429718875502008], 'avgPrecision': 0.7997943775100401, 'recall': [0.76, 0.828, 0.924, 0.744, 0.7429718875502008], 'avgRecall': 0.7997943775100401, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.736, 0.792, 0.888, 0.664, 0.7108433734939759], 'avgAccuracy': 0.7581686746987952, 'f1': [0.7395515530602483, 0.7958580641725469, 0.8958806628262571, 0.7540557053009884, 0.710800925951616], 'avgF1': 0.7792293822623314, 'precision': [0.736, 0.792, 0.888, 0.664, 0.7108433734939759], 'avgPrecision': 0.7581686746987952, 'recall': [0.736, 0.792, 0.888, 0.664, 0.7108433734939759], 'avgRecall': 0.7581686746987952, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 17, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.636, 0.708, 0.86, 0.508, 0.642570281124498], 'avgAccuracy': 0.6709140562248996, 'f1': [0.6447170018848871, 0.7192898944193061, 0.8776868010258023, 0.5632820512820512, 0.6478307986951255], 'avgF1': 0.6905613094614345, 'precision': [0.636, 0.708, 0.86, 0.508, 0.642570281124498], 'avgPrecision': 0.6709140562248996, 'recall': [0.636, 0.708, 0.86, 0.508, 0.642570281124498], 'avgRecall': 0.6709140562248996, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.616, 0.704, 0.884, 0.404, 0.5622489959839357], 'avgAccuracy': 0.6340497991967872, 'f1': [0.5735620650617654, 0.6566827155717494, 0.8679138562966777, 0.37117381745654, 0.518094152995445], 'avgF1': 0.5974853214764355, 'precision': [0.616, 0.704, 0.884, 0.404, 0.5622489959839357], 'avgPrecision': 0.6340497991967872, 'recall': [0.616, 0.704, 0.884, 0.404, 0.5622489959839357], 'avgRecall': 0.6340497991967872, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.692, 0.808, 0.884, 0.692, 0.5542168674698795], 'avgAccuracy': 0.7260433734939759, 'f1': [0.7014318364073776, 0.814857962697274, 0.8951970210072948, 0.7735882352941178, 0.5269321289585843], 'avgF1': 0.7424014368729297, 'precision': [0.692, 0.808, 0.884, 0.692, 0.5542168674698795], 'avgPrecision': 0.7260433734939759, 'recall': [0.692, 0.808, 0.884, 0.692, 0.5542168674698795], 'avgRecall': 0.7260433734939759, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.736, 0.792, 0.888, 0.74, 0.6867469879518072], 'avgAccuracy': 0.7685493975903614, 'f1': [0.7393242899049838, 0.7960645103804688, 0.8962243571242643, 0.8085083601021331, 0.6869103283973148], 'avgF1': 0.7854063691818329, 'precision': [0.736, 0.792, 0.888, 0.74, 0.6867469879518072], 'avgPrecision': 0.7685493975903614, 'recall': [0.736, 0.792, 0.888, 0.74, 0.6867469879518072], 'avgRecall': 0.7685493975903614, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.676, 0.708, 0.856, 0.38, 0.6465863453815262], 'avgAccuracy': 0.6533172690763053, 'f1': [0.6830637050005471, 0.7185833333333332, 0.8740694671919496, 0.41410980139811054, 0.6509675063891931], 'avgF1': 0.6681587626626267, 'precision': [0.676, 0.708, 0.856, 0.38, 0.6465863453815262], 'avgPrecision': 0.6533172690763053, 'recall': [0.676, 0.708, 0.856, 0.38, 0.6465863453815262], 'avgRecall': 0.6533172690763053, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.644, 0.704, 0.86, 0.552, 0.6666666666666666], 'avgAccuracy': 0.6853333333333333, 'f1': [0.652045890788748, 0.7156889232454021, 0.8776868010258023, 0.6181079617259434, 0.6697402487187393], 'avgF1': 0.706653965100927, 'precision': [0.644, 0.704, 0.86, 0.552, 0.6666666666666666], 'avgPrecision': 0.6853333333333333, 'recall': [0.644, 0.704, 0.86, 0.552, 0.6666666666666666], 'avgRecall': 0.6853333333333333, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas, Basal histiocytic cells', 'accuracy': [0.76, 0.828, 0.924, 0.744, 0.7429718875502008], 'avgAccuracy': 0.7997943775100401, 'f1': [0.7629060838668136, 0.8297918332727493, 0.9270842081345148, 0.8124973292793163, 0.7440280182396863], 'avgF1': 0.8152614945586161, 'precision': [0.76, 0.828, 0.924, 0.744, 0.7429718875502008], 'avgPrecision': 0.7997943775100401, 'recall': [0.76, 0.828, 0.924, 0.744, 0.7429718875502008], 'avgRecall': 0.7997943775100401, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.792588  0.808672   0.792588  0.792588   
1  0.752569  0.775441   0.752569  0.752569   
2  0.670914  0.690561   0.670914  0.670914   
3  0.634050  0.597485   0.634050  0.634050   
4  0.683592  0.697062   0.683592  0.683592   
5  0.751749  0.770177   0.751749  0.751749   
6  0.653317  0.668159   0.653317  0.653317   
7  0.678927  0.700084   0.678927  0.678927   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.752, 0.824, 0.924, 0.748, 0.7429718875502008], 'avgAccuracy': 0.7981943775100402, 'f1': [0.7558842972644391, 0.8258204888647473, 0.9270842081345148, 0.8154077956108745, 0.7440280182396863], 'avgF1': 0.8136449616228524, 'precision': [0.752, 0.824, 0.924, 0.748, 0.7429718875502008], 'avgPrecision': 0.7981943775100402, 'recall': [0.752, 0.824, 0.924, 0.748, 0.7429718875502008], 'avgRecall': 0.7981943775100402, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.716, 0.784, 0.884, 0.732, 0.7028112449799196], 'avgAccuracy': 0.7637622489959839, 'f1': [0.7220338382616786, 0.7917527022375216, 0.8933393937703912, 0.805721016321723, 0.7035589806674145], 'avgF1': 0.7832811862517458, 'precision': [0.716, 0.784, 0.884, 0.732, 0.7028112449799196], 'avgPrecision': 0.7637622489959839, 'recall': [0.716, 0.784, 0.884, 0.732, 0.7028112449799196], 'avgRecall': 0.7637622489959839, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 17, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.636, 0.708, 0.86, 0.508, 0.642570281124498], 'avgAccuracy': 0.6709140562248996, 'f1': [0.6447170018848871, 0.7192898944193061, 0.8776868010258023, 0.5632820512820512, 0.6484390613319435], 'avgF1': 0.6906829619887981, 'precision': [0.636, 0.708, 0.86, 0.508, 0.642570281124498], 'avgPrecision': 0.6709140562248996, 'recall': [0.636, 0.708, 0.86, 0.508, 0.642570281124498], 'avgRecall': 0.6709140562248996, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.616, 0.704, 0.88, 0.404, 0.5662650602409639], 'avgAccuracy': 0.6340530120481928, 'f1': [0.5735620650617654, 0.6566827155717494, 0.8635907682621735, 0.37117381745654, 0.5211117031664781], 'avgF1': 0.5972242139037413, 'precision': [0.616, 0.704, 0.88, 0.404, 0.5662650602409639], 'avgPrecision': 0.6340530120481928, 'recall': [0.616, 0.704, 0.88, 0.404, 0.5662650602409639], 'avgRecall': 0.6340530120481928, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgAccuracy': 0.7284433734939759, 'f1': [0.7172306323995672, 0.7682211334543151, 0.900512655353235, 0.8000670690811535, 0.5269321289585843], 'avgF1': 0.742592723849371, 'precision': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgPrecision': 0.7284433734939759, 'recall': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgRecall': 0.7284433734939759, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.708, 0.796, 0.9, 0.74, 0.7269076305220884], 'avgAccuracy': 0.7741815261044177, 'f1': [0.7111897066392177, 0.798621286641747, 0.9066318752821819, 0.8068981329839501, 0.7271057323464261], 'avgF1': 0.7900893467787046, 'precision': [0.708, 0.796, 0.9, 0.74, 0.7269076305220884], 'avgPrecision': 0.7741815261044177, 'recall': [0.708, 0.796, 0.9, 0.74, 0.7269076305220884], 'avgRecall': 0.7741815261044177, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.68, 0.72, 0.856, 0.38, 0.6465863453815262], 'avgAccuracy': 0.6565172690763053, 'f1': [0.6872763731473408, 0.7337571157495256, 0.8740694671919496, 0.41410980139811054, 0.6509675063891931], 'avgF1': 0.6720360527752239, 'precision': [0.68, 0.72, 0.856, 0.38, 0.6465863453815262], 'avgPrecision': 0.6565172690763053, 'recall': [0.68, 0.72, 0.856, 0.38, 0.6465863453815262], 'avgRecall': 0.6565172690763053, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.648, 0.708, 0.864, 0.552, 0.6626506024096386], 'avgAccuracy': 0.6869301204819277, 'f1': [0.6559943576528943, 0.7192898944193061, 0.8811160491641213, 0.6181079617259434, 0.6657520093014283], 'avgF1': 0.7080520544527387, 'precision': [0.648, 0.708, 0.864, 0.552, 0.6626506024096386], 'avgPrecision': 0.6869301204819277, 'recall': [0.648, 0.708, 0.864, 0.552, 0.6626506024096386], 'avgRecall': 0.6869301204819277, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent, Submucosal granulomas', 'accuracy': [0.752, 0.824, 0.924, 0.748, 0.7429718875502008], 'avgAccuracy': 0.7981943775100402, 'f1': [0.7558842972644391, 0.8258204888647473, 0.9270842081345148, 0.8154077956108745, 0.7440280182396863], 'avgF1': 0.8136449616228524, 'precision': [0.752, 0.824, 0.924, 0.748, 0.7429718875502008], 'avgPrecision': 0.7981943775100402, 'recall': [0.752, 0.824, 0.924, 0.748, 0.7429718875502008], 'avgRecall': 0.7981943775100402, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.795788  0.811640   0.795788  0.795788   
1  0.763762  0.783281   0.763762  0.763762   
2  0.670914  0.690683   0.670914  0.670914   
3  0.634053  0.597224   0.634053  0.634053   
4  0.694795  0.706609   0.694795  0.694795   
5  0.764537  0.781110   0.764537  0.764537   
6  0.656517  0.672036   0.656517  0.656517   
7  0.681320  0.702160   0.681320  0.681320   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.76, 0.828, 0.92, 0.748, 0.7389558232931727], 'avgAccuracy': 0.7989911646586345, 'f1': [0.7634662733409359, 0.8297918332727493, 0.9229174153355237, 0.8154077956108745, 0.7399240769564169], 'avgF1': 0.8143014789033001, 'precision': [0.76, 0.828, 0.92, 0.748, 0.7389558232931727], 'avgPrecision': 0.7989911646586345, 'recall': [0.76, 0.828, 0.92, 0.748, 0.7389558232931727], 'avgRecall': 0.7989911646586345, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.716, 0.784, 0.884, 0.74, 0.7028112449799196], 'avgAccuracy': 0.7653622489959839, 'f1': [0.7220338382616786, 0.7917527022375216, 0.8933393937703912, 0.8115889724310776, 0.7035589806674145], 'avgF1': 0.7844547774736167, 'precision': [0.716, 0.784, 0.884, 0.74, 0.7028112449799196], 'avgPrecision': 0.7653622489959839, 'recall': [0.716, 0.784, 0.884, 0.74, 0.7028112449799196], 'avgRecall': 0.7653622489959839, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 17, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.632, 0.708, 0.86, 0.508, 0.642570281124498], 'avgAccuracy': 0.6701140562248996, 'f1': [0.6407799176016208, 0.7192898944193061, 0.8776868010258023, 0.5632820512820512, 0.6484390613319435], 'avgF1': 0.6898955451321448, 'precision': [0.632, 0.708, 0.86, 0.508, 0.642570281124498], 'avgPrecision': 0.6701140562248996, 'recall': [0.632, 0.708, 0.86, 0.508, 0.642570281124498], 'avgRecall': 0.6701140562248996, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.576, 0.652, 0.856, 0.54, 0.5783132530120482], 'avgAccuracy': 0.6404626506024096, 'f1': [0.5829970584115421, 0.643024990250421, 0.8508635068635069, 0.5540894660894661, 0.5835203476582457], 'avgF1': 0.6428990738546364, 'precision': [0.576, 0.652, 0.856, 0.54, 0.5783132530120482], 'avgPrecision': 0.6404626506024096, 'recall': [0.576, 0.652, 0.856, 0.54, 0.5783132530120482], 'avgRecall': 0.6404626506024096, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgAccuracy': 0.7284433734939759, 'f1': [0.7172306323995672, 0.7682211334543151, 0.900512655353235, 0.8000670690811535, 0.5269321289585843], 'avgF1': 0.742592723849371, 'precision': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgPrecision': 0.7284433734939759, 'recall': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgRecall': 0.7284433734939759, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.748, 0.8, 0.888, 0.756, 0.6907630522088354], 'avgAccuracy': 0.776552610441767, 'f1': [0.751365472104717, 0.8057469557232404, 0.8929870618677958, 0.8209108136194143, 0.6910636189548952], 'avgF1': 0.7924147844540126, 'precision': [0.748, 0.8, 0.888, 0.756, 0.6907630522088354], 'avgPrecision': 0.776552610441767, 'recall': [0.748, 0.8, 0.888, 0.756, 0.6907630522088354], 'avgRecall': 0.776552610441767, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.68, 0.72, 0.856, 0.38, 0.6465863453815262], 'avgAccuracy': 0.6565172690763053, 'f1': [0.6872763731473408, 0.7337571157495256, 0.8740694671919496, 0.41410980139811054, 0.6509675063891931], 'avgF1': 0.6720360527752239, 'precision': [0.68, 0.72, 0.856, 0.38, 0.6465863453815262], 'avgPrecision': 0.6565172690763053, 'recall': [0.68, 0.72, 0.856, 0.38, 0.6465863453815262], 'avgRecall': 0.6565172690763053, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.648, 0.712, 0.864, 0.548, 0.6586345381526104], 'avgAccuracy': 0.6861269076305221, 'f1': [0.6559943576528943, 0.7228795043949272, 0.8811160491641213, 0.6137938849980735, 0.6615351418315487], 'avgF1': 0.707063787608313, 'precision': [0.648, 0.712, 0.864, 0.548, 0.6586345381526104], 'avgPrecision': 0.6861269076305221, 'recall': [0.648, 0.712, 0.864, 0.548, 0.6586345381526104], 'avgRecall': 0.6861269076305221, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas, Crypt abscesses extent', 'accuracy': [0.76, 0.828, 0.92, 0.748, 0.7389558232931727], 'avgAccuracy': 0.7989911646586345, 'f1': [0.7634662733409359, 0.8297918332727493, 0.9229174153355237, 0.8154077956108745, 0.7399240769564169], 'avgF1': 0.8143014789033001, 'precision': [0.76, 0.828, 0.92, 0.748, 0.7389558232931727], 'avgPrecision': 0.7989911646586345, 'recall': [0.76, 0.828, 0.92, 0.748, 0.7389558232931727], 'avgRecall': 0.7989911646586345, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.796588  0.812227   0.796588  0.796588   
1  0.765362  0.784455   0.765362  0.765362   
2  0.670114  0.689896   0.670114  0.670114   
3  0.640463  0.642899   0.640463  0.640463   
4  0.693189  0.704663   0.693189  0.693189   
5  0.751772  0.772842   0.751772  0.751772   
6  0.656517  0.672036   0.656517  0.656517   
7  0.674124  0.694803   0.674124  0.674124   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.768, 0.836, 0.916, 0.744, 0.7269076305220884], 'avgAccuracy': 0.7981815261044177, 'f1': [0.7715753130480606, 0.8370951770736254, 0.9186476489475905, 0.8124973292793163, 0.7275636856027383], 'avgF1': 0.8134758307902662, 'precision': [0.768, 0.836, 0.916, 0.744, 0.7269076305220884], 'avgPrecision': 0.7981815261044177, 'recall': [0.768, 0.836, 0.916, 0.744, 0.7269076305220884], 'avgRecall': 0.7981815261044177, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.72, 0.784, 0.888, 0.736, 0.6987951807228916], 'avgAccuracy': 0.7653590361445783, 'f1': [0.7261277665115909, 0.7911132656470891, 0.8966064363613903, 0.8086653252850436, 0.6995254587885199], 'avgF1': 0.7844076505187267, 'precision': [0.72, 0.784, 0.888, 0.736, 0.6987951807228916], 'avgPrecision': 0.7653590361445783, 'recall': [0.72, 0.784, 0.888, 0.736, 0.6987951807228916], 'avgRecall': 0.7653590361445783, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 17, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.64, 0.708, 0.864, 0.508, 0.6385542168674698], 'avgAccuracy': 0.671710843373494, 'f1': [0.6486406093810515, 0.7195034401973776, 0.8811160491641213, 0.5632820512820512, 0.6444234330806937], 'avgF1': 0.6913931166210591, 'precision': [0.64, 0.708, 0.864, 0.508, 0.6385542168674698], 'avgPrecision': 0.671710843373494, 'recall': [0.64, 0.708, 0.864, 0.508, 0.6385542168674698], 'avgRecall': 0.671710843373494, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.576, 0.656, 0.856, 0.524, 0.5823293172690763], 'avgAccuracy': 0.6388658634538152, 'f1': [0.5829970584115421, 0.6465250807983524, 0.8519605194805195, 0.536956131799571, 0.5875209339381819], 'avgF1': 0.6411919448856334, 'precision': [0.576, 0.656, 0.856, 0.524, 0.5823293172690763], 'avgPrecision': 0.6388658634538152, 'recall': [0.576, 0.656, 0.856, 0.524, 0.5823293172690763], 'avgRecall': 0.6388658634538152, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgAccuracy': 0.7284433734939759, 'f1': [0.7172306323995672, 0.7682211334543151, 0.900512655353235, 0.8000670690811535, 0.5269321289585843], 'avgF1': 0.742592723849371, 'precision': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgPrecision': 0.7284433734939759, 'recall': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgRecall': 0.7284433734939759, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.744, 0.812, 0.888, 0.716, 0.678714859437751], 'avgAccuracy': 0.7677429718875503, 'f1': [0.7470242086904276, 0.8178547530642393, 0.8962044360547426, 0.7880703658103962, 0.6783252677320752], 'avgF1': 0.7854958062703762, 'precision': [0.744, 0.812, 0.888, 0.716, 0.678714859437751], 'avgPrecision': 0.7677429718875503, 'recall': [0.744, 0.812, 0.888, 0.716, 0.678714859437751], 'avgRecall': 0.7677429718875503, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.68, 0.72, 0.856, 0.38, 0.6465863453815262], 'avgAccuracy': 0.6565172690763053, 'f1': [0.6872763731473408, 0.7337571157495256, 0.8740694671919496, 0.41410980139811054, 0.6509675063891931], 'avgF1': 0.6720360527752239, 'precision': [0.68, 0.72, 0.856, 0.38, 0.6465863453815262], 'avgPrecision': 0.6565172690763053, 'recall': [0.68, 0.72, 0.856, 0.38, 0.6465863453815262], 'avgRecall': 0.6565172690763053, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.648, 0.712, 0.868, 0.512, 0.6586345381526104], 'avgAccuracy': 0.6797269076305221, 'f1': [0.6559943576528943, 0.7235435951943827, 0.8845127718554073, 0.5718918918918919, 0.6612606849305858], 'avgF1': 0.6994406603050324, 'precision': [0.648, 0.712, 0.868, 0.512, 0.6586345381526104], 'avgPrecision': 0.6797269076305221, 'recall': [0.648, 0.712, 0.868, 0.512, 0.6586345381526104], 'avgRecall': 0.6797269076305221, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs, Lamina propria granulomas', 'accuracy': [0.768, 0.836, 0.916, 0.744, 0.7269076305220884], 'avgAccuracy': 0.7981815261044177, 'f1': [0.7715753130480606, 0.8370951770736254, 0.9186476489475905, 0.8124973292793163, 0.7275636856027383], 'avgF1': 0.8134758307902662, 'precision': [0.768, 0.836, 0.916, 0.744, 0.7269076305220884], 'avgPrecision': 0.7981815261044177, 'recall': [0.768, 0.836, 0.916, 0.744, 0.7269076305220884], 'avgRecall': 0.7981815261044177, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.792582  0.808529   0.792582  0.792582   
1  0.765359  0.784408   0.765359  0.765359   
2  0.671711  0.691393   0.671711  0.671711   
3  0.638866  0.641192   0.638866  0.638866   
4  0.697989  0.708583   0.697989  0.697989   
5  0.758940  0.777824   0.758940  0.758940   
6  0.656517  0.672036   0.656517  0.656517   
7  0.675724  0.695552   0.675724  0.675724   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.768, 0.812, 0.92, 0.744, 0.7349397590361446], 'avgAccuracy': 0.7957879518072289, 'f1': [0.7720605441890646, 0.8143268364520705, 0.9229174153355237, 0.8124973292793163, 0.7356748521718702], 'avgF1': 0.811495395485569, 'precision': [0.768, 0.812, 0.92, 0.744, 0.7349397590361446], 'avgPrecision': 0.7957879518072289, 'recall': [0.768, 0.812, 0.92, 0.744, 0.7349397590361446], 'avgRecall': 0.7957879518072289, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.732, 0.784, 0.88, 0.708, 0.6506024096385542], 'avgAccuracy': 0.7509204819277109, 'f1': [0.7361612905692113, 0.7897245344331959, 0.8894294421813149, 0.7894409937888199, 0.6522051307548435], 'avgF1': 0.7713922783454771, 'precision': [0.732, 0.784, 0.88, 0.708, 0.6506024096385542], 'avgPrecision': 0.7509204819277109, 'recall': [0.732, 0.784, 0.88, 0.708, 0.6506024096385542], 'avgRecall': 0.7509204819277109, 'params': [{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 12, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.636, 0.688, 0.86, 0.476, 0.6184738955823293], 'avgAccuracy': 0.6556947791164659, 'f1': [0.6450798281073601, 0.7007865818392135, 0.8774659373779093, 0.523989888046226, 0.6230107721593154], 'avgF1': 0.6740666015060048, 'precision': [0.636, 0.688, 0.86, 0.476, 0.6184738955823293], 'avgPrecision': 0.6556947791164659, 'recall': [0.636, 0.688, 0.86, 0.476, 0.6184738955823293], 'avgRecall': 0.6556947791164659, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.588, 0.648, 0.836, 0.556, 0.5502008032128514], 'avgAccuracy': 0.6356401606425702, 'f1': [0.5969340632518567, 0.6415611024743967, 0.83362016737868, 0.573072840203275, 0.5508502230967313], 'avgF1': 0.639207679280988, 'precision': [0.588, 0.648, 0.836, 0.556, 0.5502008032128514], 'avgPrecision': 0.6356401606425702, 'recall': [0.588, 0.648, 0.836, 0.556, 0.5502008032128514], 'avgRecall': 0.6356401606425702, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgAccuracy': 0.7284433734939759, 'f1': [0.7172306323995672, 0.7682211334543151, 0.900512655353235, 0.8000670690811535, 0.5269321289585843], 'avgF1': 0.742592723849371, 'precision': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgPrecision': 0.7284433734939759, 'recall': [0.708, 0.76, 0.892, 0.728, 0.5542168674698795], 'avgRecall': 0.7284433734939759, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.752, 0.808, 0.904, 0.74, 0.7108433734939759], 'avgAccuracy': 0.7829686746987952, 'f1': [0.7548307692307692, 0.8117360013238121, 0.9097904479888932, 0.8063585228662264, 0.7118226600985221], 'avgF1': 0.7989076803016446, 'precision': [0.752, 0.808, 0.904, 0.74, 0.7108433734939759], 'avgPrecision': 0.7829686746987952, 'recall': [0.752, 0.808, 0.904, 0.74, 0.7108433734939759], 'avgRecall': 0.7829686746987952, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.664, 0.696, 0.844, 0.508, 0.6385542168674698], 'avgAccuracy': 0.6701108433734939, 'f1': [0.6715206611570247, 0.7126574588170514, 0.8636633490747845, 0.5878097051381617, 0.6423877327491785], 'avgF1': 0.6956077813872401, 'precision': [0.664, 0.696, 0.844, 0.508, 0.6385542168674698], 'avgPrecision': 0.6701108433734939, 'recall': [0.664, 0.696, 0.844, 0.508, 0.6385542168674698], 'avgRecall': 0.6701108433734939, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.648, 0.692, 0.86, 0.496, 0.6345381526104418], 'avgAccuracy': 0.6661076305220883, 'f1': [0.6566795574097587, 0.7048120933792574, 0.8774659373779093, 0.5501711907689397, 0.6371108646946411], 'avgF1': 0.6852479287261013, 'precision': [0.648, 0.692, 0.86, 0.496, 0.6345381526104418], 'avgPrecision': 0.6661076305220883, 'recall': [0.648, 0.692, 0.86, 0.496, 0.6345381526104418], 'avgRecall': 0.6661076305220883, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis, Crypt abscesses polymorphs', 'accuracy': [0.768, 0.812, 0.92, 0.744, 0.7349397590361446], 'avgAccuracy': 0.7957879518072289, 'f1': [0.7720605441890646, 0.8143268364520705, 0.9229174153355237, 0.8124973292793163, 0.7356748521718702], 'avgF1': 0.811495395485569, 'precision': [0.768, 0.812, 0.92, 0.744, 0.7349397590361446], 'avgPrecision': 0.7957879518072289, 'recall': [0.768, 0.812, 0.92, 0.744, 0.7349397590361446], 'avgRecall': 0.7957879518072289, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.794185  0.810011   0.794185  0.794185   
1  0.741333  0.763662   0.741333  0.741333   
2  0.655695  0.674067   0.655695  0.655695   
3  0.635640  0.639208   0.635640  0.635640   
4  0.697995  0.709553   0.697995  0.697995   
5  0.750927  0.768754   0.750927  0.750927   
6  0.670111  0.695608   0.670111  0.670111   
7  0.661304  0.679928   0.661304  0.661304   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.76, 0.82, 0.916, 0.752, 0.7068273092369478], 'avgAccuracy': 0.7909654618473896, 'f1': [0.762290234628267, 0.8213907616707617, 0.9186476489475905, 0.818297837617299, 0.7067834537714055], 'avgF1': 0.8054819873270648, 'precision': [0.76, 0.82, 0.916, 0.752, 0.7068273092369478], 'avgPrecision': 0.7909654618473896, 'recall': [0.76, 0.82, 0.916, 0.752, 0.7068273092369478], 'avgRecall': 0.7909654618473896, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.732, 0.772, 0.88, 0.708, 0.678714859437751], 'avgAccuracy': 0.7541429718875502, 'f1': [0.7361612905692113, 0.7790754006968641, 0.8888647421569091, 0.7894409937888199, 0.6799353833152747], 'avgF1': 0.7746955621054158, 'precision': [0.732, 0.772, 0.88, 0.708, 0.678714859437751], 'avgPrecision': 0.7541429718875502, 'recall': [0.732, 0.772, 0.88, 0.708, 0.678714859437751], 'avgRecall': 0.7541429718875502, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 17, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.624, 0.692, 0.852, 0.476, 0.6224899598393574], 'avgAccuracy': 0.6532979919678714, 'f1': [0.6328630913680877, 0.705529492179647, 0.8706383534481813, 0.523989888046226, 0.627671002221837], 'avgF1': 0.6721383654527958, 'precision': [0.624, 0.692, 0.852, 0.476, 0.6224899598393574], 'avgPrecision': 0.6532979919678714, 'recall': [0.624, 0.692, 0.852, 0.476, 0.6224899598393574], 'avgRecall': 0.6532979919678714, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.58, 0.636, 0.836, 0.56, 0.5542168674698795], 'avgAccuracy': 0.6332433734939759, 'f1': [0.5879195229959204, 0.6296117836965294, 0.83362016737868, 0.5773853586027499, 0.5564245285234505], 'avgF1': 0.6369922722394661, 'precision': [0.58, 0.636, 0.836, 0.56, 0.5542168674698795], 'avgPrecision': 0.6332433734939759, 'recall': [0.58, 0.636, 0.836, 0.56, 0.5542168674698795], 'avgRecall': 0.6332433734939759, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.708, 0.76, 0.892, 0.688, 0.5783132530120482], 'avgAccuracy': 0.7252626506024096, 'f1': [0.7172306323995672, 0.7682211334543151, 0.900512655353235, 0.7703963533752985, 0.5643199490418153], 'avgF1': 0.7441361447248462, 'precision': [0.708, 0.76, 0.892, 0.688, 0.5783132530120482], 'avgPrecision': 0.7252626506024096, 'recall': [0.708, 0.76, 0.892, 0.688, 0.5783132530120482], 'avgRecall': 0.7252626506024096, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.74, 0.792, 0.892, 0.756, 0.6987951807228916], 'avgAccuracy': 0.7757590361445783, 'f1': [0.7437638078132016, 0.7966791942073271, 0.9008603428552633, 0.8211676695397624, 0.6990293480076913], 'avgF1': 0.7923000724846492, 'precision': [0.74, 0.792, 0.892, 0.756, 0.6987951807228916], 'avgPrecision': 0.7757590361445783, 'recall': [0.74, 0.792, 0.892, 0.756, 0.6987951807228916], 'avgRecall': 0.7757590361445783, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.656, 0.688, 0.848, 0.468, 0.642570281124498], 'avgAccuracy': 0.6605140562248996, 'f1': [0.66277455407774, 0.7013493388644024, 0.8667314670191038, 0.5391349961969208, 0.644393335847954], 'avgF1': 0.6828767384012242, 'precision': [0.656, 0.688, 0.848, 0.468, 0.642570281124498], 'avgPrecision': 0.6605140562248996, 'recall': [0.656, 0.688, 0.848, 0.468, 0.642570281124498], 'avgRecall': 0.6605140562248996, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.648, 0.692, 0.852, 0.488, 0.6385542168674698], 'avgAccuracy': 0.663710843373494, 'f1': [0.654379894761643, 0.705529492179647, 0.8706383534481813, 0.5429629629629631, 0.6416800055503369], 'avgF1': 0.6830381417805542, 'precision': [0.648, 0.692, 0.852, 0.488, 0.6385542168674698], 'avgPrecision': 0.663710843373494, 'recall': [0.648, 0.692, 0.852, 0.488, 0.6385542168674698], 'avgRecall': 0.663710843373494, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis, Initial pathologists diagnosis', 'accuracy': [0.76, 0.82, 0.916, 0.752, 0.7068273092369478], 'avgAccuracy': 0.7909654618473896, 'f1': [0.762290234628267, 0.8213907616707617, 0.9186476489475905, 0.818297837617299, 0.7067834537714055], 'avgF1': 0.8054819873270648, 'precision': [0.76, 0.82, 0.916, 0.752, 0.7068273092369478], 'avgPrecision': 0.7909654618473896, 'recall': [0.76, 0.82, 0.916, 0.752, 0.7068273092369478], 'avgRecall': 0.7909654618473896, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.788559  0.803636   0.788559  0.788559   
1  0.747740  0.769589   0.747740  0.747740   
2  0.653298  0.672138   0.653298  0.653298   
3  0.633243  0.636992   0.633243  0.633243   
4  0.692389  0.705731   0.692389  0.692389   
5  0.752537  0.770065   0.752537  0.752537   
6  0.660514  0.682877   0.660514  0.660514   
7  0.658108  0.678066   0.658108  0.658108   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.636, 0.688, 0.872, 0.628, 0.6305220883534136], 'avgAccuracy': 0.6909044176706827, 'f1': [0.6385756597908709, 0.6849219794545549, 0.8664887718528271, 0.6585342858178918, 0.6406590157105918], 'avgF1': 0.6978359425253473, 'precision': [0.636, 0.688, 0.872, 0.628, 0.6305220883534136], 'avgPrecision': 0.6909044176706827, 'recall': [0.636, 0.688, 0.872, 0.628, 0.6305220883534136], 'avgRecall': 0.6909044176706827, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.58, 0.644, 0.852, 0.56, 0.5943775100401606], 'avgAccuracy': 0.6460755020080321, 'f1': [0.5894379485985389, 0.6514146363274596, 0.8532320478581348, 0.5950850780208579, 0.6012515847052669], 'avgF1': 0.6580842591020516, 'precision': [0.58, 0.644, 0.852, 0.56, 0.5943775100401606], 'avgPrecision': 0.6460755020080321, 'recall': [0.58, 0.644, 0.852, 0.56, 0.5943775100401606], 'avgRecall': 0.6460755020080321, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 13, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.604, 0.648, 0.82, 0.464, 0.606425702811245], 'avgAccuracy': 0.628485140562249, 'f1': [0.6121555786259909, 0.6591596074671479, 0.8302300944669366, 0.49085028690662497, 0.616601711084907], 'avgF1': 0.6417994557103215, 'precision': [0.604, 0.648, 0.82, 0.464, 0.606425702811245], 'avgPrecision': 0.628485140562249, 'recall': [0.604, 0.648, 0.82, 0.464, 0.606425702811245], 'avgRecall': 0.628485140562249, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.584, 0.64, 0.836, 0.56, 0.5502008032128514], 'avgAccuracy': 0.6340401606425703, 'f1': [0.5928, 0.6331520065987755, 0.83362016737868, 0.5773853586027499, 0.5524341502470437], 'avgF1': 0.6378783365654498, 'precision': [0.584, 0.64, 0.836, 0.56, 0.5502008032128514], 'avgPrecision': 0.6340401606425703, 'recall': [0.584, 0.64, 0.836, 0.56, 0.5502008032128514], 'avgRecall': 0.6340401606425703, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.612, 0.64, 0.816, 0.444, 0.44176706827309237], 'avgAccuracy': 0.5907534136546184, 'f1': [0.605425280814179, 0.6427966813618868, 0.8173142525768375, 0.4591707833450953, 0.4352198064287803], 'avgF1': 0.5919853609053558, 'precision': [0.612, 0.64, 0.816, 0.444, 0.44176706827309237], 'avgPrecision': 0.5907534136546184, 'recall': [0.612, 0.64, 0.816, 0.444, 0.44176706827309237], 'avgRecall': 0.5907534136546184, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.54, 0.66, 0.84, 0.648, 0.6144578313253012], 'avgAccuracy': 0.6604915662650602, 'f1': [0.5427358421629521, 0.6603887842225518, 0.8409999903702634, 0.6730848465541044, 0.6249675192502576], 'avgF1': 0.6684353965120259, 'precision': [0.54, 0.66, 0.84, 0.648, 0.6144578313253012], 'avgPrecision': 0.6604915662650602, 'recall': [0.54, 0.66, 0.84, 0.648, 0.6144578313253012], 'avgRecall': 0.6604915662650602, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.608, 0.64, 0.816, 0.468, 0.5622489959839357], 'avgAccuracy': 0.6188497991967872, 'f1': [0.6172550241470635, 0.6508741829525145, 0.8235610426937862, 0.4989688912289939, 0.568540400919919], 'avgF1': 0.6318399083884554, 'precision': [0.608, 0.64, 0.816, 0.468, 0.5622489959839357], 'avgPrecision': 0.6188497991967872, 'recall': [0.608, 0.64, 0.816, 0.468, 0.5622489959839357], 'avgRecall': 0.6188497991967872, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.616, 0.648, 0.816, 0.456, 0.606425702811245], 'avgAccuracy': 0.628485140562249, 'f1': [0.6243570440480525, 0.6591596074671479, 0.8268295244578431, 0.4805616991398982, 0.61569229244606], 'avgF1': 0.6413200335118003, 'precision': [0.616, 0.648, 0.816, 0.456, 0.606425702811245], 'avgPrecision': 0.628485140562249, 'recall': [0.616, 0.648, 0.816, 0.456, 0.606425702811245], 'avgRecall': 0.628485140562249, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs, Observing pathologists diagnosis', 'accuracy': [0.636, 0.688, 0.872, 0.628, 0.6305220883534136], 'avgAccuracy': 0.6909044176706827, 'f1': [0.6385756597908709, 0.6849219794545549, 0.8664887718528271, 0.6585342858178918, 0.6406590157105918], 'avgF1': 0.6978359425253473, 'precision': [0.636, 0.688, 0.872, 0.628, 0.6305220883534136], 'avgPrecision': 0.6909044176706827, 'recall': [0.636, 0.688, 0.872, 0.628, 0.6305220883534136], 'avgRecall': 0.6909044176706827, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.686095  0.693600   0.686095  0.686095   
1  0.638879  0.651936   0.638879  0.638879   
2  0.628485  0.641799   0.628485  0.628485   
3  0.634040  0.637878   0.634040  0.634040   
4  0.553118  0.555974   0.553118  0.553118   
5  0.660492  0.668435   0.660492  0.660492   
6  0.618850  0.631840   0.618850  0.618850   
7  0.628485  0.641320   0.628485  0.628485   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.612, 0.656, 0.836, 0.616, 0.6024096385542169], 'avgAccuracy': 0.6644819277108434, 'f1': [0.6154596616440856, 0.662434581174319, 0.8393816436682746, 0.6582271851560892, 0.6147107605421194], 'avgF1': 0.6780427664369776, 'precision': [0.612, 0.656, 0.836, 0.616, 0.6024096385542169], 'avgPrecision': 0.6644819277108434, 'recall': [0.612, 0.656, 0.836, 0.616, 0.6024096385542169], 'avgRecall': 0.6644819277108434, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.596, 0.64, 0.812, 0.584, 0.6305220883534136], 'avgAccuracy': 0.6525044176706827, 'f1': [0.6015212974033538, 0.640122600619195, 0.8123749513364666, 0.6279023937865879, 0.642113948456299], 'avgF1': 0.6648070383203805, 'precision': [0.596, 0.64, 0.812, 0.584, 0.6305220883534136], 'avgPrecision': 0.6525044176706827, 'recall': [0.596, 0.64, 0.812, 0.584, 0.6305220883534136], 'avgRecall': 0.6525044176706827, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 15, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.608, 0.648, 0.816, 0.448, 0.6104417670682731], 'avgAccuracy': 0.6260883534136547, 'f1': [0.6174077861743367, 0.6591596074671479, 0.823137809332716, 0.4731226273109149, 0.6183116303598232], 'avgF1': 0.6382278921289877, 'precision': [0.608, 0.648, 0.816, 0.448, 0.6104417670682731], 'avgPrecision': 0.6260883534136547, 'recall': [0.608, 0.648, 0.816, 0.448, 0.6104417670682731], 'avgRecall': 0.6260883534136547, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.588, 0.64, 0.832, 0.564, 0.5502008032128514], 'avgAccuracy': 0.6348401606425702, 'f1': [0.595635553433596, 0.6331520065987755, 0.8278055729275242, 0.5807628708916263, 0.5524341502470437], 'avgF1': 0.6379580308197131, 'precision': [0.588, 0.64, 0.832, 0.564, 0.5502008032128514], 'avgPrecision': 0.6348401606425702, 'recall': [0.588, 0.64, 0.832, 0.564, 0.5502008032128514], 'avgRecall': 0.6348401606425702, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.592, 0.496, 0.82, 0.456, 0.41767068273092367], 'avgAccuracy': 0.5563341365461847, 'f1': [0.59580395256917, 0.5016329705367158, 0.8223921795438823, 0.4836081258482968, 0.39638393206272976], 'avgF1': 0.559964232112159, 'precision': [0.592, 0.496, 0.82, 0.456, 0.41767068273092367], 'avgPrecision': 0.5563341365461847, 'recall': [0.592, 0.496, 0.82, 0.456, 0.41767068273092367], 'avgRecall': 0.5563341365461847, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.572, 0.628, 0.816, 0.624, 0.6024096385542169], 'avgAccuracy': 0.6484819277108433, 'f1': [0.5781928918817808, 0.6367023593466424, 0.8253652661064425, 0.6665433663957044, 0.6179388852950003], 'avgF1': 0.6649485538051141, 'precision': [0.572, 0.628, 0.816, 0.624, 0.6024096385542169], 'avgPrecision': 0.6484819277108433, 'recall': [0.572, 0.628, 0.816, 0.624, 0.6024096385542169], 'avgRecall': 0.6484819277108433, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.608, 0.636, 0.812, 0.44, 0.5622489959839357], 'avgAccuracy': 0.6116497991967872, 'f1': [0.6174077861743367, 0.6472361544495773, 0.8174746257326903, 0.4624345104443084, 0.569128357858056], 'avgF1': 0.6227362869317937, 'precision': [0.608, 0.636, 0.812, 0.44, 0.5622489959839357], 'avgPrecision': 0.6116497991967872, 'recall': [0.608, 0.636, 0.812, 0.44, 0.5622489959839357], 'avgRecall': 0.6116497991967872, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.612, 0.648, 0.812, 0.44, 0.6104417670682731], 'avgAccuracy': 0.6244883534136546, 'f1': [0.6221579731743667, 0.6591596074671479, 0.8211572252926778, 0.4624345104443084, 0.6183116303598232], 'avgF1': 0.6366441893476648, 'precision': [0.612, 0.648, 0.812, 0.44, 0.6104417670682731], 'avgPrecision': 0.6244883534136546, 'recall': [0.612, 0.648, 0.812, 0.44, 0.6104417670682731], 'avgRecall': 0.6244883534136546, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion, Cryptitis polymorphs', 'accuracy': [0.612, 0.656, 0.836, 0.616, 0.6024096385542169], 'avgAccuracy': 0.6644819277108434, 'f1': [0.6154596616440856, 0.662434581174319, 0.8393816436682746, 0.6582271851560892, 0.6147107605421194], 'avgF1': 0.6780427664369776, 'precision': [0.612, 0.656, 0.836, 0.616, 0.6024096385542169], 'avgPrecision': 0.6644819277108434, 'recall': [0.612, 0.656, 0.836, 0.616, 0.6024096385542169], 'avgRecall': 0.6644819277108434, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.658876  0.672435   0.658876  0.658876   
1  0.649285  0.658750   0.649285  0.649285   
2  0.617263  0.630119   0.617263  0.617263   
3  0.634840  0.637958   0.634840  0.634840   
4  0.555524  0.561869   0.555524  0.555524   
5  0.641269  0.657025   0.641269  0.641269   
6  0.611650  0.622736   0.611650  0.611650   
7  0.617263  0.629054   0.617263  0.617263   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.612, 0.68, 0.836, 0.624, 0.5903614457831325], 'avgAccuracy': 0.6684722891566265, 'f1': [0.6154596616440856, 0.6837976441722894, 0.8393816436682746, 0.6655087309098332, 0.6014259329295256], 'avgF1': 0.6811147226648017, 'precision': [0.612, 0.68, 0.836, 0.624, 0.5903614457831325], 'avgPrecision': 0.6684722891566265, 'recall': [0.612, 0.68, 0.836, 0.624, 0.5903614457831325], 'avgRecall': 0.6684722891566265, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.584, 0.644, 0.852, 0.628, 0.5983935742971888], 'avgAccuracy': 0.6612787148594378, 'f1': [0.5915950557281217, 0.6408320672953043, 0.8462175197175196, 0.6587273383893103, 0.6022101517868623], 'avgF1': 0.6679164265834237, 'precision': [0.584, 0.644, 0.852, 0.628, 0.5983935742971888], 'avgPrecision': 0.6612787148594378, 'recall': [0.584, 0.644, 0.852, 0.628, 0.5983935742971888], 'avgRecall': 0.6612787148594378, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 15, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.616, 0.664, 0.824, 0.448, 0.6104417670682731], 'avgAccuracy': 0.6324883534136546, 'f1': [0.6262076534383155, 0.673630756752212, 0.8298178808024891, 0.4731226273109149, 0.6183116303598232], 'avgF1': 0.644218109732751, 'precision': [0.616, 0.664, 0.824, 0.448, 0.6104417670682731], 'avgPrecision': 0.6324883534136546, 'recall': [0.616, 0.664, 0.824, 0.448, 0.6104417670682731], 'avgRecall': 0.6324883534136546, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.588, 0.64, 0.832, 0.564, 0.5421686746987951], 'avgAccuracy': 0.633233734939759, 'f1': [0.595635553433596, 0.6331520065987755, 0.8278055729275242, 0.5807628708916263, 0.5444182666011156], 'avgF1': 0.6363548540905275, 'precision': [0.588, 0.64, 0.832, 0.564, 0.5421686746987951], 'avgPrecision': 0.633233734939759, 'recall': [0.588, 0.64, 0.832, 0.564, 0.5421686746987951], 'avgRecall': 0.633233734939759, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.608, 0.572, 0.756, 0.556, 0.4578313253012048], 'avgAccuracy': 0.589966265060241, 'f1': [0.6133403357183986, 0.5843540092228009, 0.7665579164150447, 0.5996817060555424, 0.44822816960521683], 'avgF1': 0.6024324274034006, 'precision': [0.608, 0.572, 0.756, 0.556, 0.4578313253012048], 'avgPrecision': 0.589966265060241, 'recall': [0.608, 0.572, 0.756, 0.556, 0.4578313253012048], 'avgRecall': 0.589966265060241, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 100, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.608, 0.628, 0.824, 0.624, 0.5823293172690763], 'avgAccuracy': 0.6532658634538152, 'f1': [0.6159049853372435, 0.6344606411549594, 0.8299149029103167, 0.6655087309098332, 0.5925869892043532], 'avgF1': 0.6676752499033412, 'precision': [0.608, 0.628, 0.824, 0.624, 0.5823293172690763], 'avgPrecision': 0.6532658634538152, 'recall': [0.608, 0.628, 0.824, 0.624, 0.5823293172690763], 'avgRecall': 0.6532658634538152, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.612, 0.648, 0.816, 0.448, 0.5622489959839357], 'avgAccuracy': 0.6172497991967871, 'f1': [0.6206146627565983, 0.6609822091650801, 0.820663491251444, 0.4731226273109149, 0.569128357858056], 'avgF1': 0.6289022696684187, 'precision': [0.612, 0.648, 0.816, 0.448, 0.5622489959839357], 'avgPrecision': 0.6172497991967871, 'recall': [0.612, 0.648, 0.816, 0.448, 0.5622489959839357], 'avgRecall': 0.6172497991967871, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.62, 0.664, 0.812, 0.436, 0.6104417670682731], 'avgAccuracy': 0.6284883534136546, 'f1': [0.6294576735092865, 0.673630756752212, 0.8211572252926778, 0.4570126259173162, 0.6183116303598232], 'avgF1': 0.6399139823662631, 'precision': [0.62, 0.664, 0.812, 0.436, 0.6104417670682731], 'avgPrecision': 0.6284883534136546, 'recall': [0.62, 0.664, 0.812, 0.436, 0.6104417670682731], 'avgRecall': 0.6284883534136546, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent, Mucin depletion', 'accuracy': [0.612, 0.68, 0.836, 0.624, 0.5903614457831325], 'avgAccuracy': 0.6684722891566265, 'f1': [0.6154596616440856, 0.6837976441722894, 0.8393816436682746, 0.6655087309098332, 0.6014259329295256], 'avgF1': 0.6811147226648017, 'precision': [0.612, 0.68, 0.836, 0.624, 0.5903614457831325], 'avgPrecision': 0.6684722891566265, 'recall': [0.612, 0.68, 0.836, 0.624, 0.5903614457831325], 'avgRecall': 0.6684722891566265, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.661282  0.674646   0.661282  0.661282   
1  0.658079  0.665500   0.658079  0.658079   
2  0.624469  0.636966   0.624469  0.624469   
3  0.633234  0.636355   0.633234  0.633234   
4  0.577966  0.589618   0.577966  0.577966   
5  0.642063  0.658495   0.642063  0.642063   
6  0.617250  0.628902   0.617250  0.617250   
7  0.628488  0.639914   0.628488  0.628488   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.596, 0.672, 0.816, 0.576, 0.6144578313253012], 'avgAccuracy': 0.6548915662650602, 'f1': [0.5988243424006806, 0.6804902698106655, 0.8234618476857828, 0.6200066234196326, 0.6307365085050349], 'avgF1': 0.6707039183643593, 'precision': [0.596, 0.672, 0.816, 0.576, 0.6144578313253012], 'avgPrecision': 0.6548915662650602, 'recall': [0.596, 0.672, 0.816, 0.576, 0.6144578313253012], 'avgRecall': 0.6548915662650602, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.616, 0.664, 0.808, 0.552, 0.6265060240963856], 'avgAccuracy': 0.6533012048192771, 'f1': [0.6203900476254813, 0.6598410901259636, 0.8074308066265284, 0.5955129357706523, 0.6373780236248979], 'avgF1': 0.6641105807547047, 'precision': [0.616, 0.664, 0.808, 0.552, 0.6265060240963856], 'avgPrecision': 0.6533012048192771, 'recall': [0.616, 0.664, 0.808, 0.552, 0.6265060240963856], 'avgRecall': 0.6533012048192771, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.612, 0.664, 0.824, 0.448, 0.6184738955823293], 'avgAccuracy': 0.6332947791164658, 'f1': [0.6221579731743667, 0.673630756752212, 0.8298178808024891, 0.4731226273109149, 0.6283064720730647], 'avgF1': 0.6454071420226095, 'precision': [0.612, 0.664, 0.824, 0.448, 0.6184738955823293], 'avgPrecision': 0.6332947791164658, 'recall': [0.612, 0.664, 0.824, 0.448, 0.6184738955823293], 'avgRecall': 0.6332947791164658, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.58, 0.632, 0.828, 0.564, 0.5582329317269076], 'avgAccuracy': 0.6324465863453815, 'f1': [0.5865654969364207, 0.6240476761524063, 0.8240385243002065, 0.5807628708916263, 0.5638815436815948], 'avgF1': 0.635859222392451, 'precision': [0.58, 0.632, 0.828, 0.564, 0.5582329317269076], 'avgPrecision': 0.6324465863453815, 'recall': [0.58, 0.632, 0.828, 0.564, 0.5582329317269076], 'avgRecall': 0.6324465863453815, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.596, 0.52, 0.764, 0.556, 0.4779116465863454], 'avgAccuracy': 0.5827823293172691, 'f1': [0.6041949365792734, 0.530273773085393, 0.7726974957015774, 0.5996817060555424, 0.48110960446741186], 'avgF1': 0.5975915031778396, 'precision': [0.596, 0.52, 0.764, 0.556, 0.4779116465863454], 'avgPrecision': 0.5827823293172691, 'recall': [0.596, 0.52, 0.764, 0.556, 0.4779116465863454], 'avgRecall': 0.5827823293172691, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 100, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.592, 0.66, 0.804, 0.576, 0.5983935742971888], 'avgAccuracy': 0.6460787148594378, 'f1': [0.5982189638318671, 0.6701104801239031, 0.8135202454395428, 0.6200066234196326, 0.6148502599839865], 'avgF1': 0.6633413145597864, 'precision': [0.592, 0.66, 0.804, 0.576, 0.5983935742971888], 'avgPrecision': 0.6460787148594378, 'recall': [0.592, 0.66, 0.804, 0.576, 0.5983935742971888], 'avgRecall': 0.6460787148594378, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.612, 0.648, 0.816, 0.448, 0.5622489959839357], 'avgAccuracy': 0.6172497991967871, 'f1': [0.6206146627565983, 0.6609822091650801, 0.820663491251444, 0.4731226273109149, 0.569128357858056], 'avgF1': 0.6289022696684187, 'precision': [0.612, 0.648, 0.816, 0.448, 0.5622489959839357], 'avgPrecision': 0.6172497991967871, 'recall': [0.612, 0.648, 0.816, 0.448, 0.5622489959839357], 'avgRecall': 0.6172497991967871, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.612, 0.664, 0.82, 0.448, 0.6224899598393574], 'avgAccuracy': 0.6332979919678715, 'f1': [0.6206146627565983, 0.673630756752212, 0.8278683716965046, 0.4731226273109149, 0.6332550509807485], 'avgF1': 0.6456982938993957, 'precision': [0.612, 0.664, 0.82, 0.448, 0.6224899598393574], 'avgPrecision': 0.6332979919678715, 'recall': [0.612, 0.664, 0.82, 0.448, 0.6224899598393574], 'avgRecall': 0.6332979919678715, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes, Cryptitis extent', 'accuracy': [0.616, 0.664, 0.808, 0.552, 0.6265060240963856], 'avgAccuracy': 0.6533012048192771, 'f1': [0.6203900476254813, 0.6598410901259636, 0.8074308066265284, 0.5955129357706523, 0.6373780236248979], 'avgF1': 0.6641105807547047, 'precision': [0.616, 0.664, 0.808, 0.552, 0.6265060240963856], 'avgPrecision': 0.6533012048192771, 'recall': [0.616, 0.664, 0.808, 0.552, 0.6265060240963856], 'avgRecall': 0.6533012048192771, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.652488  0.667513   0.652488  0.652488   
1  0.647685  0.657139   0.647685  0.647685   
2  0.623669  0.636437   0.623669  0.623669   
3  0.632447  0.635859   0.632447  0.632447   
4  0.564386  0.577860   0.564386  0.564386   
5  0.638072  0.655194   0.638072  0.638072   
6  0.617250  0.628902   0.617250  0.617250   
7  0.619666  0.632544   0.619666  0.619666   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.612, 0.656, 0.82, 0.572, 0.5863453815261044], 'avgAccuracy': 0.6492690763052209, 'f1': [0.6119110680476085, 0.6614315154745136, 0.825293917551982, 0.6160093896713615, 0.5979155777156289], 'avgF1': 0.6625122936922189, 'precision': [0.612, 0.656, 0.82, 0.572, 0.5863453815261044], 'avgPrecision': 0.6492690763052209, 'recall': [0.612, 0.656, 0.82, 0.572, 0.5863453815261044], 'avgRecall': 0.6492690763052209, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.608, 0.644, 0.816, 0.616, 0.5983935742971888], 'avgAccuracy': 0.6564787148594378, 'f1': [0.6147536412237607, 0.6388821699639169, 0.8132218486994254, 0.6479808553004147, 0.5994998325845738], 'avgF1': 0.6628676695544183, 'precision': [0.608, 0.644, 0.816, 0.616, 0.5983935742971888], 'avgPrecision': 0.6564787148594378, 'recall': [0.608, 0.644, 0.816, 0.616, 0.5983935742971888], 'avgRecall': 0.6564787148594378, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.612, 0.636, 0.816, 0.44, 0.606425702811245], 'avgAccuracy': 0.622085140562249, 'f1': [0.6206146627565983, 0.6481743688445316, 0.823137809332716, 0.4624345104443084, 0.6156927843674831], 'avgF1': 0.6340108271491275, 'precision': [0.612, 0.636, 0.816, 0.44, 0.606425702811245], 'avgPrecision': 0.622085140562249, 'recall': [0.612, 0.636, 0.816, 0.44, 0.606425702811245], 'avgRecall': 0.622085140562249, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.58, 0.62, 0.828, 0.548, 0.5582329317269076], 'avgAccuracy': 0.6268465863453815, 'f1': [0.5865654969364207, 0.6126864253393665, 0.8240385243002065, 0.5633286073460746, 0.5638815436815948], 'avgF1': 0.6301001195207326, 'precision': [0.58, 0.62, 0.828, 0.548, 0.5582329317269076], 'avgPrecision': 0.6268465863453815, 'recall': [0.58, 0.62, 0.828, 0.548, 0.5582329317269076], 'avgRecall': 0.6268465863453815, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.592, 0.512, 0.772, 0.576, 0.5140562248995983], 'avgAccuracy': 0.5932112449799196, 'f1': [0.6013965705791793, 0.5194229795405669, 0.7819369555965385, 0.6200066234196326, 0.5122329339196808], 'avgF1': 0.6069992126111197, 'precision': [0.592, 0.512, 0.772, 0.576, 0.5140562248995983], 'avgPrecision': 0.5932112449799196, 'recall': [0.592, 0.512, 0.772, 0.576, 0.5140562248995983], 'avgRecall': 0.5932112449799196, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 50, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.612, 0.64, 0.804, 0.588, 0.5783132530120482], 'avgAccuracy': 0.6444626506024096, 'f1': [0.6186772155048018, 0.6478162423004679, 0.8120479137588448, 0.6328363748840087, 0.5904526089271125], 'avgF1': 0.6603660710750471, 'precision': [0.612, 0.64, 0.804, 0.588, 0.5783132530120482], 'avgPrecision': 0.6444626506024096, 'recall': [0.612, 0.64, 0.804, 0.588, 0.5783132530120482], 'avgRecall': 0.6444626506024096, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.604, 0.624, 0.816, 0.292, 0.5622489959839357], 'avgAccuracy': 0.5796497991967872, 'f1': [0.61177165200391, 0.6384276488131646, 0.820663491251444, 0.21934272300469487, 0.567510305627972], 'avgF1': 0.5715431641402371, 'precision': [0.604, 0.624, 0.816, 0.292, 0.5622489959839357], 'avgPrecision': 0.5796497991967872, 'recall': [0.604, 0.624, 0.816, 0.292, 0.5622489959839357], 'avgRecall': 0.5796497991967872, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.612, 0.636, 0.82, 0.428, 0.606425702811245], 'avgAccuracy': 0.620485140562249, 'f1': [0.6214339960985303, 0.6481743688445316, 0.8278683716965046, 0.44600938967136156, 0.61633588694935], 'avgF1': 0.6319644026520557, 'precision': [0.612, 0.636, 0.82, 0.428, 0.606425702811245], 'avgPrecision': 0.620485140562249, 'recall': [0.612, 0.636, 0.82, 0.428, 0.606425702811245], 'avgRecall': 0.620485140562249, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface, Epithelial changes', 'accuracy': [0.612, 0.64, 0.804, 0.588, 0.5783132530120482], 'avgAccuracy': 0.6444626506024096, 'f1': [0.6186772155048018, 0.6478162423004679, 0.8120479137588448, 0.6328363748840087, 0.5904526089271125], 'avgF1': 0.6603660710750471, 'precision': [0.612, 0.64, 0.804, 0.588, 0.5783132530120482], 'avgPrecision': 0.6444626506024096, 'recall': [0.612, 0.64, 0.804, 0.588, 0.5783132530120482], 'avgRecall': 0.6444626506024096, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.646066  0.659952   0.646066  0.646066   
1  0.631679  0.636787   0.631679  0.631679   
2  0.615656  0.628102   0.615656  0.615656   
3  0.626847  0.630100   0.626847  0.626847   
4  0.574011  0.585532   0.574011  0.574011   
5  0.631656  0.647171   0.631656  0.631656   
6  0.579650  0.571543   0.579650  0.579650   
7  0.618885  0.630340   0.618885  0.618885   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.592, 0.656, 0.824, 0.504, 0.5943775100401606], 'avgAccuracy': 0.6340755020080321, 'f1': [0.5980212001558565, 0.6670598013750955, 0.8285229373035318, 0.5425714225562643, 0.6084503671880669], 'avgF1': 0.648925145715763, 'precision': [0.592, 0.656, 0.824, 0.504, 0.5943775100401606], 'avgPrecision': 0.6340755020080321, 'recall': [0.592, 0.656, 0.824, 0.504, 0.5943775100401606], 'avgRecall': 0.6340755020080321, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.6, 0.652, 0.82, 0.548, 0.6265060240963856], 'avgAccuracy': 0.6493012048192771, 'f1': [0.6033029892890382, 0.6473497705879956, 0.8167160452450775, 0.5913085349705068, 0.6361123606252389], 'avgF1': 0.6589579401435715, 'precision': [0.6, 0.652, 0.82, 0.548, 0.6265060240963856], 'avgPrecision': 0.6493012048192771, 'recall': [0.6, 0.652, 0.82, 0.548, 0.6265060240963856], 'avgRecall': 0.6493012048192771, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.604, 0.636, 0.812, 0.44, 0.606425702811245], 'avgAccuracy': 0.619685140562249, 'f1': [0.61177165200391, 0.6481743688445316, 0.8197477983298336, 0.4624345104443084, 0.6156927843674831], 'avgF1': 0.6315642227980134, 'precision': [0.604, 0.636, 0.812, 0.44, 0.606425702811245], 'avgPrecision': 0.619685140562249, 'recall': [0.604, 0.636, 0.812, 0.44, 0.606425702811245], 'avgRecall': 0.619685140562249, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.58, 0.636, 0.836, 0.564, 0.5622489959839357], 'avgAccuracy': 0.6356497991967871, 'f1': [0.5872706671400955, 0.6289701363993127, 0.8315358895946051, 0.5807628708916263, 0.5693116807913918], 'avgF1': 0.6395702489634063, 'precision': [0.58, 0.636, 0.836, 0.564, 0.5622489959839357], 'avgPrecision': 0.6356497991967871, 'recall': [0.58, 0.636, 0.836, 0.564, 0.5622489959839357], 'avgRecall': 0.6356497991967871, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.488, 0.484, 0.824, 0.592, 0.46987951807228917], 'avgAccuracy': 0.5715759036144579, 'f1': [0.4881854033290653, 0.4840335046023092, 0.8255368421052631, 0.636703889102811, 0.4722604017572089], 'avgF1': 0.5813440081793315, 'precision': [0.488, 0.484, 0.824, 0.592, 0.46987951807228917], 'avgPrecision': 0.5715759036144579, 'recall': [0.488, 0.484, 0.824, 0.592, 0.46987951807228917], 'avgRecall': 0.5715759036144579, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.588, 0.636, 0.808, 0.492, 0.5863453815261044], 'avgAccuracy': 0.6220690763052209, 'f1': [0.5949933723480304, 0.6476024849007765, 0.8163210653450873, 0.5284336320956039, 0.5999121353702886], 'avgF1': 0.6374525380119573, 'precision': [0.588, 0.636, 0.808, 0.492, 0.5863453815261044], 'avgPrecision': 0.6220690763052209, 'recall': [0.588, 0.636, 0.808, 0.492, 0.5863453815261044], 'avgRecall': 0.6220690763052209, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.604, 0.624, 0.816, 0.292, 0.5622489959839357], 'avgAccuracy': 0.5796497991967872, 'f1': [0.615447354904982, 0.6384276488131646, 0.820663491251444, 0.21934272300469487, 0.567510305627972], 'avgF1': 0.5722783047204515, 'precision': [0.604, 0.624, 0.816, 0.292, 0.5622489959839357], 'avgPrecision': 0.5796497991967872, 'recall': [0.604, 0.624, 0.816, 0.292, 0.5622489959839357], 'avgRecall': 0.5796497991967872, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.608, 0.636, 0.812, 0.432, 0.6104417670682731], 'avgAccuracy': 0.6196883534136546, 'f1': [0.6174077861743367, 0.6481743688445316, 0.8211572252926778, 0.4515378449559144, 0.6211745715404445], 'avgF1': 0.631890359361581, 'precision': [0.608, 0.636, 0.812, 0.432, 0.6104417670682731], 'avgPrecision': 0.6196883534136546, 'recall': [0.608, 0.636, 0.812, 0.432, 0.6104417670682731], 'avgRecall': 0.6196883534136546, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?, Mucosal surface', 'accuracy': [0.6, 0.652, 0.82, 0.548, 0.6265060240963856], 'avgAccuracy': 0.6493012048192771, 'f1': [0.6033029892890382, 0.6473497705879956, 0.8167160452450775, 0.5913085349705068, 0.6361123606252389], 'avgF1': 0.6589579401435715, 'precision': [0.6, 0.652, 0.82, 0.548, 0.6265060240963856], 'avgPrecision': 0.6493012048192771, 'recall': [0.6, 0.652, 0.82, 0.548, 0.6265060240963856], 'avgRecall': 0.6493012048192771, 'params': [{'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.631672  0.646086   0.631672  0.631672   
1  0.638092  0.646084   0.638092  0.638092   
2  0.613256  0.625492   0.613256  0.613256   
3  0.635650  0.639570   0.635650  0.635650   
4  0.571576  0.581344   0.571576  0.571576   
5  0.622069  0.637453   0.622069  0.622069   
6  0.579650  0.572278   0.579650  0.579650   
7  0.619688  0.631890   0.619688  0.619688   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6, 0.652, 0.82, 0.5, 0.5742971887550201], 'avgAccuracy': 0.629259437751004, 'f1': [0.6093232293303404, 0.6633460869565218, 0.8251485214553067, 0.5379012815632536, 0.5915770514856514], 'avgF1': 0.6454592341582148, 'precision': [0.6, 0.652, 0.82, 0.5, 0.5742971887550201], 'avgPrecision': 0.629259437751004, 'recall': [0.6, 0.652, 0.82, 0.5, 0.5742971887550201], 'avgRecall': 0.629259437751004, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.604, 0.672, 0.82, 0.588, 0.6024096385542169], 'avgAccuracy': 0.6572819277108434, 'f1': [0.6052661985293564, 0.6572731560083004, 0.8156253537068479, 0.6219027230046948, 0.5983607541911871], 'avgF1': 0.6596856370880773, 'precision': [0.604, 0.672, 0.82, 0.588, 0.6024096385542169], 'avgPrecision': 0.6572819277108434, 'recall': [0.604, 0.672, 0.82, 0.588, 0.6024096385542169], 'avgRecall': 0.6572819277108434, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.604, 0.628, 0.824, 0.44, 0.6024096385542169], 'avgAccuracy': 0.6196819277108434, 'f1': [0.6145893939393938, 0.6422275032353206, 0.8269461764042407, 0.4624345104443084, 0.6071360014189862], 'avgF1': 0.6306667170884499, 'precision': [0.604, 0.628, 0.824, 0.44, 0.6024096385542169], 'avgPrecision': 0.6196819277108434, 'recall': [0.604, 0.628, 0.824, 0.44, 0.6024096385542169], 'avgRecall': 0.6196819277108434, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.588, 0.636, 0.868, 0.496, 0.5542168674698795], 'avgAccuracy': 0.6284433734939759, 'f1': [0.5948966346627165, 0.6327143850643705, 0.8567190476190477, 0.5035256216651566, 0.5498298389375885], 'avgF1': 0.6275371055897759, 'precision': [0.588, 0.636, 0.868, 0.496, 0.5542168674698795], 'avgPrecision': 0.6284433734939759, 'recall': [0.588, 0.636, 0.868, 0.496, 0.5542168674698795], 'avgRecall': 0.6284433734939759, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.472, 0.5, 0.752, 0.584, 0.40963855421686746], 'avgAccuracy': 0.5435277108433735, 'f1': [0.4720463104509248, 0.5104181724315953, 0.7621985238959469, 0.6279023937865879, 0.39875834386196113], 'avgF1': 0.5542647488854032, 'precision': [0.472, 0.5, 0.752, 0.584, 0.40963855421686746], 'avgPrecision': 0.5435277108433735, 'recall': [0.472, 0.5, 0.752, 0.584, 0.40963855421686746], 'avgRecall': 0.5435277108433735, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 300, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.6, 0.644, 0.812, 0.512, 0.5742971887550201], 'avgAccuracy': 0.628459437751004, 'f1': [0.6102253571117878, 0.655694554498395, 0.8173248611924149, 0.5517871674491394, 0.5915770514856514], 'avgF1': 0.6453217983474777, 'precision': [0.6, 0.644, 0.812, 0.512, 0.5742971887550201], 'avgPrecision': 0.628459437751004, 'recall': [0.6, 0.644, 0.812, 0.512, 0.5742971887550201], 'avgRecall': 0.628459437751004, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.604, 0.624, 0.816, 0.292, 0.5622489959839357], 'avgAccuracy': 0.5796497991967872, 'f1': [0.615447354904982, 0.6384276488131646, 0.820663491251444, 0.21934272300469487, 0.567510305627972], 'avgF1': 0.5722783047204515, 'precision': [0.604, 0.624, 0.816, 0.292, 0.5622489959839357], 'avgPrecision': 0.5796497991967872, 'recall': [0.604, 0.624, 0.816, 0.292, 0.5622489959839357], 'avgRecall': 0.5796497991967872, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.608, 0.628, 0.82, 0.428, 0.6024096385542169], 'avgAccuracy': 0.6172819277108433, 'f1': [0.6196229060339602, 0.6422275032353206, 0.8238198674191084, 0.44600938967136156, 0.6081786499842641], 'avgF1': 0.6279716632688029, 'precision': [0.608, 0.628, 0.82, 0.428, 0.6024096385542169], 'avgPrecision': 0.6172819277108433, 'recall': [0.608, 0.628, 0.82, 0.428, 0.6024096385542169], 'avgRecall': 0.6172819277108433, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture, Patchy lamina propria cellularity?', 'accuracy': [0.604, 0.672, 0.82, 0.588, 0.6024096385542169], 'avgAccuracy': 0.6572819277108434, 'f1': [0.6052661985293564, 0.6572731560083004, 0.8156253537068479, 0.6219027230046948, 0.5983607541911871], 'avgF1': 0.6596856370880773, 'precision': [0.604, 0.672, 0.82, 0.588, 0.6024096385542169], 'avgPrecision': 0.6572819277108434, 'recall': [0.604, 0.672, 0.82, 0.588, 0.6024096385542169], 'avgRecall': 0.6572819277108434, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.627659  0.644022   0.627659  0.627659   
1  0.654885  0.658808   0.654885  0.654885   
2  0.613253  0.623950   0.613253  0.613253   
3  0.628443  0.627537   0.628443  0.628443   
4  0.543528  0.554265   0.543528  0.543528   
5  0.623663  0.639526   0.623663  0.623663   
6  0.579650  0.572278   0.579650  0.579650   
7  0.614082  0.625779   0.614082  0.614082   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.592, 0.632, 0.8, 0.496, 0.5742971887550201], 'avgAccuracy': 0.618859437751004, 'f1': [0.6025583455737991, 0.6460102162145839, 0.8093474893769824, 0.5331888768508487, 0.5899516433079256], 'avgF1': 0.6362113142648279, 'precision': [0.592, 0.632, 0.8, 0.496, 0.5742971887550201], 'avgPrecision': 0.618859437751004, 'recall': [0.592, 0.632, 0.8, 0.496, 0.5742971887550201], 'avgRecall': 0.618859437751004, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.596, 0.632, 0.804, 0.6, 0.5863453815261044], 'avgAccuracy': 0.6436690763052209, 'f1': [0.6045842026825634, 0.6344738479868622, 0.8026538687119332, 0.6433103343407273, 0.5921571780624428], 'avgF1': 0.6554358863569058, 'precision': [0.596, 0.632, 0.804, 0.6, 0.5863453815261044], 'avgPrecision': 0.6436690763052209, 'recall': [0.596, 0.632, 0.804, 0.6, 0.5863453815261044], 'avgRecall': 0.6436690763052209, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.608, 0.648, 0.824, 0.456, 0.6024096385542169], 'avgAccuracy': 0.6276819277108434, 'f1': [0.6111510180148313, 0.652914821737252, 0.8269461764042407, 0.4836081258482968, 0.6071360014189862], 'avgF1': 0.6363512286847214, 'precision': [0.608, 0.648, 0.824, 0.456, 0.6024096385542169], 'avgPrecision': 0.6276819277108434, 'recall': [0.608, 0.648, 0.824, 0.456, 0.6024096385542169], 'avgRecall': 0.6276819277108434, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.588, 0.656, 0.852, 0.496, 0.5461847389558233], 'avgAccuracy': 0.6276369477911646, 'f1': [0.5819666666666667, 0.632737139610698, 0.8424039352136963, 0.5035256216651566, 0.5345075462702669], 'avgF1': 0.6190281818852968, 'precision': [0.588, 0.656, 0.852, 0.496, 0.5461847389558233], 'avgPrecision': 0.6276369477911646, 'recall': [0.588, 0.656, 0.852, 0.496, 0.5461847389558233], 'avgRecall': 0.6276369477911646, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.472, 0.504, 0.752, 0.564, 0.44176706827309237], 'avgAccuracy': 0.5467534136546185, 'f1': [0.4720463104509248, 0.5149442877908903, 0.7621985238959469, 0.6079141515761235, 0.42298549663666013], 'avgF1': 0.5560177540701091, 'precision': [0.472, 0.504, 0.752, 0.564, 0.44176706827309237], 'avgPrecision': 0.5467534136546185, 'recall': [0.472, 0.504, 0.752, 0.564, 0.44176706827309237], 'avgRecall': 0.5467534136546185, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.592, 0.624, 0.796, 0.512, 0.5662650602409639], 'avgAccuracy': 0.6180530120481927, 'f1': [0.6025583455737991, 0.6384033162670809, 0.8057957459172967, 0.5517871674491394, 0.581965662542169], 'avgF1': 0.636102047549897, 'precision': [0.592, 0.624, 0.796, 0.512, 0.5662650602409639], 'avgPrecision': 0.6180530120481927, 'recall': [0.592, 0.624, 0.796, 0.512, 0.5662650602409639], 'avgRecall': 0.6180530120481927, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.612, 0.648, 0.816, 0.292, 0.570281124497992], 'avgAccuracy': 0.5876562248995983, 'f1': [0.6175520476208417, 0.652914821737252, 0.820663491251444, 0.21934272300469487, 0.5731214468372557], 'avgF1': 0.5767189060902976, 'precision': [0.612, 0.648, 0.816, 0.292, 0.570281124497992], 'avgPrecision': 0.5876562248995983, 'recall': [0.612, 0.648, 0.816, 0.292, 0.570281124497992], 'avgRecall': 0.5876562248995983, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.612, 0.652, 0.824, 0.456, 0.6024096385542169], 'avgAccuracy': 0.6292819277108433, 'f1': [0.6175520476208417, 0.6563668903803132, 0.8269461764042407, 0.4836081258482968, 0.6071360014189862], 'avgF1': 0.6383218483345358, 'precision': [0.612, 0.652, 0.824, 0.456, 0.6024096385542169], 'avgPrecision': 0.6292819277108433, 'recall': [0.612, 0.652, 0.824, 0.456, 0.6024096385542169], 'avgRecall': 0.6292819277108433, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 7000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs, Crypt architecture', 'accuracy': [0.596, 0.632, 0.804, 0.6, 0.5863453815261044], 'avgAccuracy': 0.6436690763052209, 'f1': [0.6045842026825634, 0.6344738479868622, 0.8026538687119332, 0.6433103343407273, 0.5921571780624428], 'avgF1': 0.6554358863569058, 'precision': [0.596, 0.632, 0.804, 0.6, 0.5863453815261044], 'avgPrecision': 0.6436690763052209, 'recall': [0.596, 0.632, 0.804, 0.6, 0.5863453815261044], 'avgRecall': 0.6436690763052209, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.618859  0.636211   0.618859  0.618859   
1  0.630069  0.641429   0.630069  0.630069   
2  0.627682  0.636351   0.627682  0.627682   
3  0.627637  0.619028   0.627637  0.627637   
4  0.545131  0.554708   0.545131  0.545131   
5  0.609256  0.625372   0.609256  0.609256   
6  0.587656  0.576719   0.587656  0.587656   
7  0.627682  0.637006   0.627682  0.627682   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.592, 0.632, 0.796, 0.48, 0.570281124497992], 'avgAccuracy': 0.6140562248995984, 'f1': [0.6025583455737991, 0.6460102162145839, 0.8057957459172967, 0.5139049349862618, 0.5848924745213447], 'avgF1': 0.6306323434426573, 'precision': [0.592, 0.632, 0.796, 0.48, 0.570281124497992], 'avgPrecision': 0.6140562248995984, 'recall': [0.592, 0.632, 0.796, 0.48, 0.570281124497992], 'avgRecall': 0.6140562248995984, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.608, 0.632, 0.804, 0.528, 0.5903614457831325], 'avgAccuracy': 0.6324722891566266, 'f1': [0.6159929066149162, 0.6344738479868622, 0.8026538687119332, 0.5697357361051315, 0.5812855992398632], 'avgF1': 0.6408283917317413, 'precision': [0.608, 0.632, 0.804, 0.528, 0.5903614457831325], 'avgPrecision': 0.6324722891566266, 'recall': [0.608, 0.632, 0.804, 0.528, 0.5903614457831325], 'avgRecall': 0.6324722891566266, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 15, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.6, 0.648, 0.82, 0.456, 0.5943775100401606], 'avgAccuracy': 0.6236755020080321, 'f1': [0.6034940009149274, 0.652914821737252, 0.8238198674191084, 0.4836081258482968, 0.5978977036266419], 'avgF1': 0.6323469039092453, 'precision': [0.6, 0.648, 0.82, 0.456, 0.5943775100401606], 'avgPrecision': 0.6236755020080321, 'recall': [0.6, 0.648, 0.82, 0.456, 0.5943775100401606], 'avgRecall': 0.6236755020080321, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.58, 0.632, 0.844, 0.456, 0.5461847389558233], 'avgAccuracy': 0.6116369477911646, 'f1': [0.5744997626957759, 0.6129490392648288, 0.8352144200155431, 0.4499424200395074, 0.5345075462702669], 'avgF1': 0.6014226376571844, 'precision': [0.58, 0.632, 0.844, 0.456, 0.5461847389558233], 'avgPrecision': 0.6116369477911646, 'recall': [0.58, 0.632, 0.844, 0.456, 0.5461847389558233], 'avgRecall': 0.6116369477911646, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.472, 0.504, 0.764, 0.548, 0.43775100401606426], 'avgAccuracy': 0.5451502008032129, 'f1': [0.4720463104509248, 0.5149442877908903, 0.7742345271829176, 0.5913085349705068, 0.4175335401186784], 'avgF1': 0.5540134401027836, 'precision': [0.472, 0.504, 0.764, 0.548, 0.43775100401606426], 'avgPrecision': 0.5451502008032129, 'recall': [0.472, 0.504, 0.764, 0.548, 0.43775100401606426], 'avgRecall': 0.5451502008032129, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.592, 0.624, 0.792, 0.488, 0.5662650602409639], 'avgAccuracy': 0.6124530120481928, 'f1': [0.6025583455737991, 0.6384033162670809, 0.8021974584555229, 0.5236349604476173, 0.5798018919438358], 'avgF1': 0.6293191945375712, 'precision': [0.592, 0.624, 0.792, 0.488, 0.5662650602409639], 'avgPrecision': 0.6124530120481928, 'recall': [0.592, 0.624, 0.792, 0.488, 0.5662650602409639], 'avgRecall': 0.6124530120481928, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.604, 0.644, 0.804, 0.292, 0.5662650602409639], 'avgAccuracy': 0.5820530120481928, 'f1': [0.6097664328009156, 0.6494525687406804, 0.8109900803264056, 0.21934272300469487, 0.5690426103506999], 'avgF1': 0.5717188830446793, 'precision': [0.604, 0.644, 0.804, 0.292, 0.5662650602409639], 'avgPrecision': 0.5820530120481928, 'recall': [0.604, 0.644, 0.804, 0.292, 0.5662650602409639], 'avgRecall': 0.5820530120481928, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.6, 0.648, 0.82, 0.456, 0.6024096385542169], 'avgAccuracy': 0.6252819277108433, 'f1': [0.6034940009149274, 0.652914821737252, 0.8238198674191084, 0.4836081258482968, 0.59884451614511], 'avgF1': 0.6325362664129389, 'precision': [0.6, 0.648, 0.82, 0.456, 0.6024096385542169], 'avgPrecision': 0.6252819277108433, 'recall': [0.6, 0.648, 0.82, 0.456, 0.6024096385542169], 'avgRecall': 0.6252819277108433, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity, Lamina propria polymorphs', 'accuracy': [0.6, 0.648, 0.82, 0.456, 0.6024096385542169], 'avgAccuracy': 0.6252819277108433, 'f1': [0.6034940009149274, 0.652914821737252, 0.8238198674191084, 0.4836081258482968, 0.59884451614511], 'avgF1': 0.6325362664129389, 'precision': [0.6, 0.648, 0.82, 0.456, 0.6024096385542169], 'avgPrecision': 0.6252819277108433, 'recall': [0.6, 0.648, 0.82, 0.456, 0.6024096385542169], 'avgRecall': 0.6252819277108433, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.612453  0.629072   0.612453  0.612453   
1  0.616472  0.622974   0.616472  0.616472   
2  0.623676  0.632347   0.623676  0.623676   
3  0.611637  0.601423   0.611637  0.611637   
4  0.539528  0.548849   0.539528  0.539528   
5  0.610853  0.627152   0.610853  0.610853   
6  0.582053  0.571719   0.582053  0.582053   
7  0.623679  0.632686   0.623679  0.623679   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.596, 0.628, 0.792, 0.472, 0.570281124497992], 'avgAccuracy': 0.6116562248995984, 'f1': [0.607280840862991, 0.6422275032353206, 0.8021974584555229, 0.5039938857953925, 0.5854759230033727], 'avgF1': 0.6282351222705199, 'precision': [0.596, 0.628, 0.792, 0.472, 0.570281124497992], 'avgPrecision': 0.6116562248995984, 'recall': [0.596, 0.628, 0.792, 0.472, 0.570281124497992], 'avgRecall': 0.6116562248995984, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.596, 0.628, 0.808, 0.532, 0.5903614457831325], 'avgAccuracy': 0.6308722891566265, 'f1': [0.607280840862991, 0.6301735142946531, 0.8061945586440107, 0.5741253317003471, 0.5831697615830446], 'avgF1': 0.6401888014170093, 'precision': [0.596, 0.628, 0.808, 0.532, 0.5903614457831325], 'avgPrecision': 0.6308722891566265, 'recall': [0.596, 0.628, 0.808, 0.532, 0.5903614457831325], 'avgRecall': 0.6308722891566265, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 16, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.604, 0.644, 0.816, 0.456, 0.5983935742971888], 'avgAccuracy': 0.6236787148594377, 'f1': [0.6097664328009156, 0.6494525687406804, 0.820663491251444, 0.4836081258482968, 0.5932320083470138], 'avgF1': 0.6313445253976702, 'precision': [0.604, 0.644, 0.816, 0.456, 0.5983935742971888], 'avgPrecision': 0.6236787148594377, 'recall': [0.604, 0.644, 0.816, 0.456, 0.5983935742971888], 'avgRecall': 0.6236787148594377, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.58, 0.628, 0.844, 0.476, 0.5461847389558233], 'avgAccuracy': 0.6148369477911646, 'f1': [0.5744997626957759, 0.6293084896615351, 0.8352144200155431, 0.47792207792207797, 0.5345075462702669], 'avgF1': 0.6102904593130398, 'precision': [0.58, 0.628, 0.844, 0.476, 0.5461847389558233], 'avgPrecision': 0.6148369477911646, 'recall': [0.58, 0.628, 0.844, 0.476, 0.5461847389558233], 'avgRecall': 0.6148369477911646, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.604, 0.636, 0.82, 0.456, 0.5020080321285141], 'avgAccuracy': 0.6036016064257028, 'f1': [0.6106450134127736, 0.6416151060887352, 0.8238198674191084, 0.4836081258482968, 0.5003608089566262], 'avgF1': 0.612009784345108, 'precision': [0.604, 0.636, 0.82, 0.456, 0.5020080321285141], 'avgPrecision': 0.6036016064257028, 'recall': [0.604, 0.636, 0.82, 0.456, 0.5020080321285141], 'avgRecall': 0.6036016064257028, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.596, 0.624, 0.796, 0.484, 0.5742971887550201], 'avgAccuracy': 0.614859437751004, 'f1': [0.607280840862991, 0.6384033162670809, 0.8057957459172967, 0.5187922642890985, 0.5904819311782841], 'avgF1': 0.6321508197029503, 'precision': [0.596, 0.624, 0.796, 0.484, 0.5742971887550201], 'avgPrecision': 0.614859437751004, 'recall': [0.596, 0.624, 0.796, 0.484, 0.5742971887550201], 'avgRecall': 0.614859437751004, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.596, 0.612, 0.8, 0.292, 0.5662650602409639], 'avgAccuracy': 0.5732530120481928, 'f1': [0.603806913598197, 0.6213139091237966, 0.8076898561163416, 0.21934272300469487, 0.5690426103506999], 'avgF1': 0.564239202438746, 'precision': [0.596, 0.612, 0.8, 0.292, 0.5662650602409639], 'avgPrecision': 0.5732530120481928, 'recall': [0.596, 0.612, 0.8, 0.292, 0.5662650602409639], 'avgRecall': 0.5732530120481928, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.604, 0.644, 0.816, 0.456, 0.6024096385542169], 'avgAccuracy': 0.6244819277108433, 'f1': [0.6097664328009156, 0.6494525687406804, 0.820663491251444, 0.4836081258482968, 0.59884451614511], 'avgF1': 0.6324670269572894, 'precision': [0.604, 0.644, 0.816, 0.456, 0.6024096385542169], 'avgPrecision': 0.6244819277108433, 'recall': [0.604, 0.644, 0.816, 0.456, 0.6024096385542169], 'avgRecall': 0.6244819277108433, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?, Marked & transmucosal increase in lamina propria cellularity', 'accuracy': [0.604, 0.644, 0.816, 0.456, 0.6024096385542169], 'avgAccuracy': 0.6244819277108433, 'f1': [0.6097664328009156, 0.6494525687406804, 0.820663491251444, 0.4836081258482968, 0.59884451614511], 'avgF1': 0.6324670269572894, 'precision': [0.604, 0.644, 0.816, 0.456, 0.6024096385542169], 'avgPrecision': 0.6244819277108433, 'recall': [0.604, 0.644, 0.816, 0.456, 0.6024096385542169], 'avgRecall': 0.6244819277108433, 'params': [{'activation': 'logistic', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.611656  0.628235   0.611656  0.611656   
1  0.620472  0.624168   0.620472  0.620472   
2  0.623679  0.631345   0.623679  0.623679   
3  0.614837  0.610290   0.614837  0.614837   
4  0.592379  0.602067   0.592379  0.592379   
5  0.612456  0.629404   0.612456  0.612456   
6  0.573253  0.564239   0.573253  0.573253   
7  0.624482  0.632467   0.624482  0.624482   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.612, 0.648, 0.812, 0.544, 0.5742971887550201], 'avgAccuracy': 0.638059437751004, 'f1': [0.6175520476208417, 0.652914821737252, 0.8174746257326903, 0.5870680448931069, 0.579512717536814], 'avgF1': 0.650904451504141, 'precision': [0.612, 0.648, 0.812, 0.544, 0.5742971887550201], 'avgPrecision': 0.638059437751004, 'recall': [0.612, 0.648, 0.812, 0.544, 0.5742971887550201], 'avgRecall': 0.638059437751004, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.612, 0.648, 0.832, 0.536, 0.5903614457831325], 'avgAccuracy': 0.6436722891566266, 'f1': [0.6175520476208417, 0.6354157216990197, 0.8233563097823132, 0.578476922138894, 0.5831697615830446], 'avgF1': 0.6475941525648227, 'precision': [0.612, 0.648, 0.832, 0.536, 0.5903614457831325], 'avgPrecision': 0.6436722891566266, 'recall': [0.612, 0.648, 0.832, 0.536, 0.5903614457831325], 'avgRecall': 0.6436722891566266, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.596, 0.612, 0.816, 0.512, 0.5903614457831325], 'avgAccuracy': 0.6252722891566265, 'f1': [0.603806913598197, 0.6213139091237966, 0.820663491251444, 0.5517871674491394, 0.5938344471001414], 'avgF1': 0.6382811857045437, 'precision': [0.596, 0.612, 0.816, 0.512, 0.5903614457831325], 'avgPrecision': 0.6252722891566265, 'recall': [0.596, 0.612, 0.816, 0.512, 0.5903614457831325], 'avgRecall': 0.6252722891566265, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.608, 0.644, 0.808, 0.292, 0.6024096385542169], 'avgAccuracy': 0.5908819277108434, 'f1': [0.6136632411067194, 0.6494525687406804, 0.8142509200967953, 0.21934272300469487, 0.609131163348031], 'avgF1': 0.5811681232593842, 'precision': [0.608, 0.644, 0.808, 0.292, 0.6024096385542169], 'avgPrecision': 0.5908819277108434, 'recall': [0.608, 0.644, 0.808, 0.292, 0.6024096385542169], 'avgRecall': 0.5908819277108434, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.612, 0.64, 0.816, 0.532, 0.5020080321285141], 'avgAccuracy': 0.6204016064257029, 'f1': [0.6175520476208417, 0.6451239443465581, 0.820663491251444, 0.5741253317003471, 0.5003608089566262], 'avgF1': 0.6315651247751634, 'precision': [0.612, 0.64, 0.816, 0.532, 0.5020080321285141], 'avgPrecision': 0.6204016064257029, 'recall': [0.612, 0.64, 0.816, 0.532, 0.5020080321285141], 'avgRecall': 0.6204016064257029, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.612, 0.648, 0.812, 0.552, 0.570281124497992], 'avgAccuracy': 0.6388562248995984, 'f1': [0.6175520476208417, 0.652914821737252, 0.8174746257326903, 0.5955129357706523, 0.5747513399198295], 'avgF1': 0.6516411541562531, 'precision': [0.612, 0.648, 0.812, 0.552, 0.570281124497992], 'avgPrecision': 0.6388562248995984, 'recall': [0.612, 0.648, 0.812, 0.552, 0.570281124497992], 'avgRecall': 0.6388562248995984, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.596, 0.604, 0.8, 0.292, 0.5622489959839357], 'avgAccuracy': 0.5708497991967871, 'f1': [0.603806913598197, 0.6135424430157802, 0.8076898561163416, 0.21934272300469487, 0.5685455107141854], 'avgF1': 0.5625854892898399, 'precision': [0.596, 0.604, 0.8, 0.292, 0.5622489959839357], 'avgPrecision': 0.5708497991967871, 'recall': [0.596, 0.604, 0.8, 0.292, 0.5622489959839357], 'avgRecall': 0.5708497991967871, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.596, 0.612, 0.816, 0.516, 0.5943775100401606], 'avgAccuracy': 0.6268755020080321, 'f1': [0.603806913598197, 0.6213139091237966, 0.820663491251444, 0.5563338734471728, 0.598970573889464], 'avgF1': 0.6402177522620148, 'precision': [0.596, 0.612, 0.816, 0.516, 0.5943775100401606], 'avgPrecision': 0.6268755020080321, 'recall': [0.596, 0.612, 0.816, 0.516, 0.5943775100401606], 'avgRecall': 0.6268755020080321, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch, Increased lamina propria cellularity?', 'accuracy': [0.612, 0.648, 0.832, 0.536, 0.5903614457831325], 'avgAccuracy': 0.6436722891566266, 'f1': [0.6175520476208417, 0.6354157216990197, 0.8233563097823132, 0.578476922138894, 0.5831697615830446], 'avgF1': 0.6475941525648227, 'precision': [0.612, 0.648, 0.832, 0.536, 0.5903614457831325], 'avgPrecision': 0.6436722891566266, 'recall': [0.612, 0.648, 0.832, 0.536, 0.5903614457831325], 'avgRecall': 0.6436722891566266, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 14, 'p': 2, 'weights': 'distance'}]}
*****************************************************

                    model                                           features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch, ...   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch, ...   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch, ...   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch, ...   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch, ...   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch, ...   
6                     SVC  Active inflammation?, Severity of Crypt Arch, ...   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch, ...   

   accuracy        f1  precision    recall  \
0  0.638059  0.650904   0.638059  0.638059   
1  0.643672  0.647594   0.643672  0.643672   
2  0.623676  0.637623   0.623676  0.623676   
3  0.590882  0.581168   0.590882  0.590882   
4  0.601179  0.614478   0.601179  0.601179   
5  0.635656  0.648034   0.635656  0.635656   
6  0.570850  0.562585   0.570850  0.570850   
7  0.626076  0.639308   0.626076  0.626076   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.608, 0.652, 0.852, 0.584, 0.5742971887550201], 'avgAccuracy': 0.6540594377510041, 'f1': [0.6116425814420257, 0.6406507043708826, 0.83827602905569, 0.6033470327141214, 0.579512717536814], 'avgF1': 0.6546858130239067, 'precision': [0.608, 0.652, 0.852, 0.584, 0.5742971887550201], 'avgPrecision': 0.6540594377510041, 'recall': [0.608, 0.652, 0.852, 0.584, 0.5742971887550201], 'avgRecall': 0.6540594377510041, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.608, 0.652, 0.852, 0.552, 0.570281124497992], 'avgAccuracy': 0.6468562248995984, 'f1': [0.6116425814420257, 0.6406507043708826, 0.83827602905569, 0.5692674719748956, 0.5747513399198295], 'avgF1': 0.6469176253526646, 'precision': [0.608, 0.652, 0.852, 0.552, 0.570281124497992], 'avgPrecision': 0.6468562248995984, 'recall': [0.608, 0.652, 0.852, 0.552, 0.570281124497992], 'avgRecall': 0.6468562248995984, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 13, 'p': 2, 'weights': 'distance'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.588, 0.616, 0.852, 0.54, 0.5622489959839357], 'avgAccuracy': 0.6316497991967871, 'f1': [0.5944057833791824, 0.6087414978791758, 0.83827602905569, 0.5558656093169368, 0.5640271031415182], 'avgF1': 0.6322632045545006, 'precision': [0.588, 0.616, 0.852, 0.54, 0.5622489959839357], 'avgPrecision': 0.6316497991967871, 'recall': [0.588, 0.616, 0.852, 0.54, 0.5622489959839357], 'avgRecall': 0.6316497991967871, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.588, 0.612, 0.844, 0.316, 0.5662650602409639], 'avgAccuracy': 0.5852530120481928, 'f1': [0.5941312373598087, 0.6051415129837867, 0.8311853496980323, 0.2188744588744589, 0.568920292268031], 'avgF1': 0.5636505702368235, 'precision': [0.588, 0.612, 0.844, 0.316, 0.5662650602409639], 'avgPrecision': 0.5852530120481928, 'recall': [0.588, 0.612, 0.844, 0.316, 0.5662650602409639], 'avgRecall': 0.5852530120481928, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.608, 0.652, 0.852, 0.556, 0.46987951807228917], 'avgAccuracy': 0.6275759036144578, 'f1': [0.6116425814420257, 0.6406507043708826, 0.83827602905569, 0.573657067570111, 0.4689321632521648], 'avgF1': 0.6266317091381748, 'precision': [0.608, 0.652, 0.852, 0.556, 0.46987951807228917], 'avgPrecision': 0.6275759036144578, 'recall': [0.608, 0.652, 0.852, 0.556, 0.46987951807228917], 'avgRecall': 0.6275759036144578, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.608, 0.652, 0.852, 0.584, 0.5783132530120482], 'avgAccuracy': 0.6548626506024097, 'f1': [0.6116425814420257, 0.6406507043708826, 0.83827602905569, 0.6033470327141214, 0.585221839739912], 'avgF1': 0.6558276374645263, 'precision': [0.608, 0.652, 0.852, 0.584, 0.5783132530120482], 'avgPrecision': 0.6548626506024097, 'recall': [0.608, 0.652, 0.852, 0.584, 0.5783132530120482], 'avgRecall': 0.6548626506024097, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.608, 0.648, 0.852, 0.316, 0.5622489959839357], 'avgAccuracy': 0.5972497991967871, 'f1': [0.6116425814420257, 0.6371394795299579, 0.83827602905569, 0.2188744588744589, 0.5640271031415182], 'avgF1': 0.5739919304087301, 'precision': [0.608, 0.648, 0.852, 0.316, 0.5622489959839357], 'avgPrecision': 0.5972497991967871, 'recall': [0.608, 0.648, 0.852, 0.316, 0.5622489959839357], 'avgRecall': 0.5972497991967871, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.592, 0.616, 0.852, 0.54, 0.5622489959839357], 'avgAccuracy': 0.6324497991967871, 'f1': [0.5982050258684406, 0.6087414978791758, 0.83827602905569, 0.5558656093169368, 0.5640271031415182], 'avgF1': 0.6330230530523523, 'precision': [0.592, 0.616, 0.852, 0.54, 0.5622489959839357], 'avgPrecision': 0.6324497991967871, 'recall': [0.592, 0.616, 0.852, 0.54, 0.5622489959839357], 'avgRecall': 0.6324497991967871, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 9000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?, Severity of Crypt Arch', 'accuracy': [0.608, 0.652, 0.852, 0.584, 0.5783132530120482], 'avgAccuracy': 0.6548626506024097, 'f1': [0.6116425814420257, 0.6406507043708826, 0.83827602905569, 0.6033470327141214, 0.585221839739912], 'avgF1': 0.6558276374645263, 'precision': [0.608, 0.652, 0.852, 0.584, 0.5783132530120482], 'avgPrecision': 0.6548626506024097, 'recall': [0.608, 0.652, 0.852, 0.584, 0.5783132530120482], 'avgRecall': 0.6548626506024097, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

                    model                                      features  \
0  RandomForestClassifier  Active inflammation?, Severity of Crypt Arch   
1    KNeighborsClassifier  Active inflammation?, Severity of Crypt Arch   
2      LogisticRegression  Active inflammation?, Severity of Crypt Arch   
3              GaussianNB  Active inflammation?, Severity of Crypt Arch   
4      AdaBoostClassifier  Active inflammation?, Severity of Crypt Arch   
5  DecisionTreeClassifier  Active inflammation?, Severity of Crypt Arch   
6                     SVC  Active inflammation?, Severity of Crypt Arch   
7           MLPClassifier  Active inflammation?, Severity of Crypt Arch   

   accuracy        f1  precision    recall  \
0  0.653256  0.653734   0.653256  0.653256   
1  0.646856  0.646918   0.646856  0.646856   
2  0.629250  0.630178   0.629250  0.629250   
3  0.585253  0.563651   0.585253  0.585253   
4  0.611576  0.612555   0.611576  0.611576   
5  0.652456  0.652907   0.652456  0.652456   
6  0.597250  0.573992   0.597250  0.597250   
7  0.631650  0.632114   0.631650  0.631650   

                                              params  
0  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2  {'C': 1, 'class_weight': None, 'dual': False, ...  
3           {'priors': None, 'var_smoothing': 1e-09}  
4  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7  {'activation': 'logistic', 'alpha': 0.0001, 'b...  

Processing Model: RandomForestClassifier

*****************************************************
* RandomForestClassifier
* Best Params Result: 
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgAccuracy': 0.5347437751004016, 'f1': [0.4562137637692224, 0.5376398451739745, 0.7760531930160248, 0.21244813278008298, 0.3596271517630594], 'avgF1': 0.4683964173004728, 'precision': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgPrecision': 0.5347437751004016, 'recall': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgRecall': 0.5347437751004016, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: KNeighborsClassifier

*****************************************************
* KNeighborsClassifier
* Best Params Result: 
* {'classifier': 'KNeighborsClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgAccuracy': 0.5347437751004016, 'f1': [0.4562137637692224, 0.5376398451739745, 0.7760531930160248, 0.21244813278008298, 0.3596271517630594], 'avgF1': 0.4683964173004728, 'precision': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgPrecision': 0.5347437751004016, 'recall': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgRecall': 0.5347437751004016, 'params': [{'algorithm': 'brute', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 13, 'p': 2, 'weights': 'uniform'}]}
*****************************************************

Processing Model: LogisticRegression

*****************************************************
* LogisticRegression
* Best Params Result: 
* {'classifier': 'LogisticRegression', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgAccuracy': 0.5347437751004016, 'f1': [0.4562137637692224, 0.5376398451739745, 0.7760531930160248, 0.21244813278008298, 0.3596271517630594], 'avgF1': 0.4683964173004728, 'precision': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgPrecision': 0.5347437751004016, 'recall': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgRecall': 0.5347437751004016, 'params': [{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'ovr', 'n_jobs': -1, 'penalty': 'l2', 'random_state': None, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}]}
*****************************************************

Processing Model: GaussianNB

*****************************************************
* GaussianNB
* Best Params Result: 
* {'classifier': 'GaussianNB', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgAccuracy': 0.5347437751004016, 'f1': [0.4562137637692224, 0.5376398451739745, 0.7760531930160248, 0.21244813278008298, 0.3596271517630594], 'avgF1': 0.4683964173004728, 'precision': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgPrecision': 0.5347437751004016, 'recall': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgRecall': 0.5347437751004016, 'params': [{'priors': None, 'var_smoothing': 1e-09}]}
*****************************************************

Processing Model: AdaBoostClassifier

*****************************************************
* AdaBoostClassifier
* Best Params Result: 
* {'classifier': 'AdaBoostClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgAccuracy': 0.5347437751004016, 'f1': [0.4562137637692224, 0.5376398451739745, 0.7760531930160248, 0.21244813278008298, 0.3596271517630594], 'avgF1': 0.4683964173004728, 'precision': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgPrecision': 0.5347437751004016, 'recall': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgRecall': 0.5347437751004016, 'params': [{'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1, 'n_estimators': 20, 'random_state': None}]}
*****************************************************

Processing Model: DecisionTreeClassifier

*****************************************************
* DecisionTreeClassifier
* Best Params Result: 
* {'classifier': 'DecisionTreeClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgAccuracy': 0.5347437751004016, 'f1': [0.4562137637692224, 0.5376398451739745, 0.7760531930160248, 0.21244813278008298, 0.3596271517630594], 'avgF1': 0.4683964173004728, 'precision': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgPrecision': 0.5347437751004016, 'recall': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgRecall': 0.5347437751004016, 'params': [{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}]}
*****************************************************

Processing Model: SVC

*****************************************************
* SVC
* Best Params Result: 
* {'classifier': 'SVC', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgAccuracy': 0.5347437751004016, 'f1': [0.4562137637692224, 0.5376398451739745, 0.7760531930160248, 0.21244813278008298, 0.3596271517630594], 'avgF1': 0.4683964173004728, 'precision': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgPrecision': 0.5347437751004016, 'recall': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgRecall': 0.5347437751004016, 'params': [{'C': 1.0, 'break_ties': False, 'cache_size': 4000, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'scale', 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}]}
*****************************************************

Processing Model: MLPClassifier

*****************************************************
* MLPClassifier
* Best Params Result: 
* {'classifier': 'MLPClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgAccuracy': 0.5347437751004016, 'f1': [0.4562137637692224, 0.5376398451739745, 0.7760531930160248, 0.21244813278008298, 0.3596271517630594], 'avgF1': 0.4683964173004728, 'precision': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgPrecision': 0.5347437751004016, 'recall': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgRecall': 0.5347437751004016, 'params': [{'activation': 'identity', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 5000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}]}
*****************************************************
*****************************************************
* Best Performing Model and Params is:
* {'classifier': 'RandomForestClassifier', 'features': 'Active inflammation?', 'accuracy': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgAccuracy': 0.5347437751004016, 'f1': [0.4562137637692224, 0.5376398451739745, 0.7760531930160248, 0.21244813278008298, 0.3596271517630594], 'avgF1': 0.4683964173004728, 'precision': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgPrecision': 0.5347437751004016, 'recall': [0.508, 0.6, 0.816, 0.32, 0.42971887550200805], 'avgRecall': 0.5347437751004016, 'params': [{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': -1, 'oob_score': True, 'random_state': None, 'verbose': 0, 'warm_start': False}]}
*****************************************************

                    model              features  accuracy        f1  \
0  RandomForestClassifier  Active inflammation?  0.534744  0.468396   
1    KNeighborsClassifier  Active inflammation?  0.534744  0.468396   
2      LogisticRegression  Active inflammation?  0.534744  0.468396   
3              GaussianNB  Active inflammation?  0.534744  0.468396   
4      AdaBoostClassifier  Active inflammation?  0.534744  0.468396   
5  DecisionTreeClassifier  Active inflammation?  0.534744  0.468396   
6                     SVC  Active inflammation?  0.534744  0.468396   
7           MLPClassifier  Active inflammation?  0.534744  0.468396   

   precision    recall                                             params  
0   0.534744  0.534744  {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...  
1   0.534744  0.534744  {'algorithm': 'brute', 'leaf_size': 30, 'metri...  
2   0.534744  0.534744  {'C': 1, 'class_weight': None, 'dual': False, ...  
3   0.534744  0.534744           {'priors': None, 'var_smoothing': 1e-09}  
4   0.534744  0.534744  {'algorithm': 'SAMME.R', 'base_estimator': Non...  
5   0.534744  0.534744  {'ccp_alpha': 0.0, 'class_weight': None, 'crit...  
6   0.534744  0.534744  {'C': 1.0, 'break_ties': False, 'cache_size': ...  
7   0.534744  0.534744  {'activation': 'logistic', 'alpha': 0.0001, 'b...
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Results/Discussion

Results

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Visualizations

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Discussion

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Conclusions

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